System, method, and apparatus for providing dynamic, prioritized spectrum management and utilization

ABSTRACT

Systems, methods, and apparatuses for providing dynamic, prioritized spectrum utilization management. The system includes at least one monitoring sensor, at least one data analysis engine, at least one application, a semantic engine, a programmable rules and policy editor, a tip and cue server, and/or a control panel. The tip and cue server is operable utilize the environmental awareness from the data processed by the at least one data analysis engine in combination with additional information to create actionable data.

CROSS REFERENCES TO RELATED APPLICATIONS

This application is related to and claims priority from the followingU.S. patents and patent applications. This application is a continuationof U.S. patent application Ser. No. 17/985,570, filed Nov. 11, 2022,which is a continuation-in-part of U.S. patent application Ser. No.17/691,683, filed Mar. 10, 2022, which is a continuation of U.S. patentapplication Ser. No. 17/470,253, filed Sep. 9, 2021, which is acontinuation of U.S. application Ser. No. 17/085,635, filed Oct. 30,2020, which claims the benefit of U.S. Provisional Application No.63/018,929, filed May 1, 2020. Each of the above listed applications isincorporated herein by reference in its entirety.

This application is also related to the following U.S. patentapplications: U.S. patent application Ser. No. 17/529,995, filed Nov.18, 2021; U.S. patent application Ser. No. 17/674,482, filed Feb. 17,2022; U.S. patent application Ser. No. 17/687,149, filed Mar. 4, 2022;U.S. patent application Ser. No. 17/691,670, filed Mar. 10, 2022; U.S.patent application Ser. No. 17/691,683, filed Mar. 10, 2022; U.S. patentapplication Ser. No. 17/695,370, filed Mar. 15, 2022; U.S. patentapplication Ser. No. 17/901,330, filed Sep. 1, 2022; and U.S. patentapplication Ser. No. 17/901,354, filed Sep. 1, 2022.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates to spectrum analysis and management forelectromagnetic signals, and more particularly for providing dynamic,prioritized spectrum utilization management.

2. Description of the Prior Art

It is generally known in the prior art to provide wirelesscommunications spectrum management for detecting devices and formanaging the space. Spectrum management includes the process ofregulating the use of radio frequencies to promote efficient use andgain net social benefit. A problem faced in effective spectrummanagement is the various numbers of devices emanating wireless signalpropagations at different frequencies and across different technologicalstandards. Coupled with the different regulations relating to spectrumusage around the globe effective spectrum management becomes difficultto obtain and at best can only be reached over a long period of time.

Another problem facing effective spectrum management is the growing needfrom spectrum despite the finite amount of spectrum available. Wirelesstechnologies and applications or services that require spectrum haveexponentially grown in recent years. Consequently, available spectrumhas become a valuable resource that must be efficiently utilized.Therefore, systems and methods are needed to effectively manage andoptimize the available spectrum that is being used.

Prior art patent documents include the following:

U.S. Patent Publication No. 2018/0352441 for Devices, methods, andsystems with dynamic spectrum sharing by inventors Zheng, et al., filedJun. 4, 2018 and published Dec. 6, 2018, is directed to devices,methods, and systems with dynamic spectrum sharing. A wirelesscommunication device includes a software-defined radio, a spectrumsensing sub-system, a memory, and an electronic processor. Thesoftware-defined radio is configured to generate an input signal, andwirelessly communicate with one or more radio nodes using a traffic datachannel and a broadcast control channel. The spectrum sensing sub-systemis configured to sense local spectrum information from the input signal.The electronic processor is communicatively connected to the memory andthe spectrum sensing sub-system and is configured to receive the localspectrum information from the spectrum sensing sub-system, receivespectrum information from the one or more radio nodes, and allocateresources for the traffic data channel based on the local spectruminformation and the spectrum information that is received from the oneor more radio nodes.

U.S. Patent Publication No. 2018/0295607 for Method and apparatus foradaptive bandwidth usage in a wireless communication network byinventors Lindoff, et al., filed Oct. 10, 2017 and published Oct. 11,2018, is directed to reconfiguration of a receiver bandwidth of thewireless device is initiated to match the second scheduling bandwidth,wherein the second scheduling bandwidth is larger than a firstscheduling bandwidth currently associated with the wireless device, andwherein the first and second scheduling bandwidths respectively definethe bandwidth used for scheduling transmissions to the wireless device.

U.S. Pat. No. 9,538,528 for Efficient co-existence method for dynamicspectrum sharing by inventors Wagner, et al., filed Oct. 6, 2011 andissued Jan. 3, 2017, is directed to an apparatus that defines a set ofresources out of a first number of orthogonal radio resources andcontrols a transmitting means to simultaneously transmit a respectivefirst radio signal for each resource on all resources of the set. Arespective estimated interference is estimated on each of the resourcesof the set when the respective first radio signals are transmittedsimultaneously. A first resource of the set is selected if the estimatedinterference on the first resource exceeds a first predefined level and,in the set, the first resource is replaced by a second resource of thefirst number of resources not having been part of the set. Each of thecontrolling and the estimating, the selecting, and the replacing isperformed in order, respectively, for a predefined time.

U.S. Pat. No. 8,972,311 for Intelligent spectrum allocation based onuser behavior patterns by inventors Srikanteswara, et al., filed Jun.26, 2012 and issued Mar. 3, 2015, is directed to a platform tofacilitate transferring spectrum rights is provided that includes adatabase to ascertain information regarding available spectrum for usein wireless communications. A request for spectrum use from an entityneeding spectrum may be matched with available spectrum. This matchingcomprises determining a pattern in user requests overtime to optimizespectrum allocation. The Cloud Spectrum Services (CSS) process allowsentities to access spectrum they would otherwise not have; it allows theend user to complete their download during congested periods whilemaintaining high service quality; and it allows the holder of rentalspectrum to receive compensation for an otherwise idle asset.

U.S. Pat. No. 10,536,210 for Interference suppressing method and devicein dynamic frequency spectrum access system by inventors Zhao, et al.,filed Apr. 14, 2016 and issued Jan. 14, 2020, is directed to aninterference suppressing method and device in a dynamic frequencyspectrum access (DSA) system. The system includes: a frequency spectrummanagement device, a primary system including a plurality of primarydevices, and a secondary system including a plurality of secondarydevices. The method includes: transmitting position information of eachof the secondary devices to the frequency spectrum management device;determining, by the frequency spectrum management device, a weightfactor for a specific secondary device according to the receivedposition formation; and performing a second-stage precoding, and in thesecond-stage precoding, adjusting, by using the weight factor, anestimated power of the specific secondary device leaking to the othersecondary device.

U.S. Pat. No. 10,582,401 for Large scale radio frequency signalinformation processing and analysis system by inventors Mengwasser, etal., filed Apr. 15, 2019 and issued Mar. 3, 2020, is directed to alarge-scale radio frequency signal information processing and analysissystem that provides advanced signal analysis for telecommunicationapplications, including band capacity and geographical densitydeterminations and detection, classification, identification, andgeolocation of signals across a wide range of frequencies and acrossbroad geographical areas. The system may utilize a range of novelalgorithms for bin-wise processing, Rayleigh distribution analysis,telecommunication signal classification, receiver anomaly detection,transmitter density estimation, transmitter detection and location,geolocation analysis, telecommunication activity estimation,telecommunication utilization estimation, frequency utilizationestimation, and data interpolation.

U.S. Pat. No. 10,070,444 for Coordinated spectrum allocation andde-allocation to minimize spectrum fragmentation in a cognitive radionetwork by inventors Markwart, et al., filed Dec. 2, 2011 and issuedSep. 4, 2018, is directed to an apparatus and a method by which afragmentation probability is determined which indicates a probability offragmentation of frequency resources in at least one network section forat least one network operating entity. Moreover, an apparatus and amethod by which frequency resources in at least one network section areallocated and/or de-allocated, priorities of frequency resources aredefined for at least one network operating entity individually, andallocating and/or de-allocating of the frequency resources for the atleast one network operating entity is performed based on the priorities.For allocating and/or de-allocating of the frequency resources, also thefragmentation probability may be taken into account.

U.S. Patent Publication No. 2020/0007249 for Wireless signal monitoringand analysis, and related methods, systems, and devices by inventorsDerr, et al., filed Sep. 12, 2019 and published Jan. 2, 2020, isdirected to wireless signal classifiers and systems that incorporate thesame may include an energy-based detector configured to analyze anentire set of measurements and generate a first single classificationresult, a cyclostationary-based detector configured to analyze less thanthe entire set of measurements and generate a second signalclassification result; and a classification merger configured to mergethe first signal classification result and the second signalclassification result. Ensemble wireless signal classification andsystems and devices the incorporate the same are disclosed. Someensemble wireless signal classification may include energy-basedclassification processes and machine learning-based classificationprocesses. Incremental machine learning techniques may be incorporatedto add new machine learning-based classifiers to a system or updateexisting machine learning-based classifiers.

U.S. Patent Publication No. 2018/0324595 for Spectral sensing andallocation using deep machine learning by inventor Shima, filed May 7,2018 and published Nov. 8, 2018, is directed to methods and systems foridentifying occupied areas of a radio frequency (RF) spectrum,identifying areas within that RF spectrum that are unusable for furthertransmissions, and identifying areas within that RF spectrum that areoccupied but that may nonetheless be available for additional RFtransmissions are provided. Implementation of the method then systemscan include the use of multiple deep neural networks (DNNs), such asconvolutional neural networks (CNN's), that are provided with inputs inthe form of RF spectrograms. Embodiments of the present disclosure canbe applied to cognitive radios or other configurable communicationdevices, including but not limited to multiple inputs multiple output(MIMO) devices and 5G communication system devices.

U.S. Patent Publication No. 2017/0041802 for Spectrum resourcemanagement device and method by inventors Sun, et al., filed May 27,2015 and published Feb. 9, 2017, is directed to a spectrum resourcemanagement device: determines available spectrum resources of a targetcommunication system, so that aggregation interference caused by thetarget communication system and a communication system with a low rightagainst a communication system with a high right in a management areadoes not exceed an interference threshold of the communication systemwith a high right; reduces available spectrum resources of thecommunication system with a low right, so that the interference causedby the communication system with a low right against the targetcommunication system does not exceed an interference threshold of thetarget communication system; and updates the available spectrumresources of the target communication system according to the reducedavailable spectrum resources of the communication system with a lowright, so that the aggregation interference does not exceed theinterference threshold of the communication system with a high right.

U.S. Pat. No. 9,900,899 for Dynamic spectrum allocation method anddynamic spectrum allocation device by inventors Jiang, et al., filedMar. 26, 2014 and issued Feb. 20, 2018, is directed to a dynamicspectrum allocation method and a dynamic spectrum allocation device. Inthe method, a centralized node performs spectrum allocation andtransmits a spectrum allocation result to each communication node, sothat the communication node operates at a corresponding spectrumresource in accordance with the spectrum allocation result and performsstatistics of communication quality measurement information. Thecentralized node receives the communication quality measurementinformation reported by the communication node, and determines whetheror not it is required to trigger the spectrum re-allocation for thecommunication node in accordance with the communication qualitymeasurement information about the communication node. When it isrequired to trigger the spectrum re-allocation, the centralized nodere-allocates the spectrum for the communication node.

U.S. Pat. No. 9,578,516 for Radio system and spectrum resourcereconfiguration method thereof by inventors Liu, et al., filed Feb. 7,2013 and issued Feb. 21, 2017, is directed to a radio system and aspectrum resource reconfiguration method thereof. The method comprises:a Reconfigurable Base Station (RBS) divides subordinate nodes intogroups according to attributes of the subordinate nodes, and sends areconfiguration command to a subordinate node in a designated group, andthe RBS and the subordinate node execute reconfiguration of spectrumresources according to the reconfiguration command; or, the RBS executesreconfiguration of spectrum resources according to the reconfigurationcommand; and a subordinate User Equipment (UE) accessing to areconfigured RBS after interruption. The reconfiguration of spectrumresources of a cognitive radio system can be realized.

U.S. Pat. No. 9,408,210 for Method, device and system for dynamicfrequency spectrum optimization by inventors Pikhletsky, et al., filedFeb. 25, 2014 and issued Aug. 2, 2016, is directed to a method, a deviceand a system for dynamic frequency spectrum optimization. The methodincludes: predicting a traffic distribution of terminal(s) in each cellof multiple cells; generating multiple frequency spectrum allocationschemes for the multiple cells according to the traffic distribution ofthe terminal(s) in each cell, wherein each frequency spectrum allocationscheme comprises frequency spectrum(s) allocated for each cell;selecting a frequency spectrum allocation scheme superior to a currentfrequency spectrum allocation scheme of the multiple cells from themultiple frequency spectrum allocation schemes according to at least twonetwork performance indicators of a network in which the multiple cellsare located; and allocating frequency spectrum(s) for the multiple cellsusing the selected frequency spectrum allocation scheme. This improvesthe utilization rate of the frequency spectrum and optimizes themultiple network performance indicators at the same time.

U.S. Pat. No. 9,246,576 for Apparatus and methods for dynamic spectrumallocation in satellite communications by inventors Yanai, et al., filedMar. 5, 2012 and issued Jan. 26, 2016, is directed to a communicationsystem including Satellite Communication apparatus providingcommunication services to at least a first set of communicants, thefirst set of communicants including a first plurality of communicants,wherein the communication services are provided to each of thecommunicants in accordance with a spectrum allocation correspondingthereto, thereby to define a first plurality of spectrum allocationsapportioning a first predefined spectrum portion among the first set ofcommunicants; and Dynamic Spectrum Allocations apparatus operative todynamically modify at least one spectrum allocation corresponding to atleast one of the first plurality of communicants without exceeding thespectrum portion.

U.S. Pat. No. 8,254,393 for Harnessing predictive models of durations ofchannel availability for enhanced opportunistic allocation of radiospectrum by inventor Horvitz, filed Jun. 29, 2007 and issued Aug. 28,2012, is directed to a proactive adaptive radio methodology for theopportunistic allocation of radio spectrum is described. The methods canbe used to allocate radio spectrum resources by employing machinelearning to learn models, via accruing data over time, that have theability to predict the context-sensitive durations of the availabilityof channels. The predictive models are combined with decision-theoreticcost-benefit analyses to minimize disruptions of service or quality thatcan be associated with reactive allocation policies. Rather thanreacting to losses of channel, the proactive policies seek switches inadvance of the loss of a channel. Beyond determining durations ofavailability for one or more frequency bands statistical machinelearning also be employed to generate price predictions in order tofacilitate a sale or rental of the available frequencies, and thesepredictions can be employed in the switching analyses. The methods canbe employed in non-cooperating distributed models of allocation, incentralized allocation approaches, and in hybrid spectrum allocationscenarios.

U.S. Pat. No. 6,990,087 for Dynamic wireless resource utilization byinventors Rao, et al., filed Apr. 22, 2003 and issued Jan. 24, 2006, isdirected to a method for dynamic wireless resource utilization includesmonitoring a wireless communication resource; generating wirelesscommunication resource data; using the wireless communication resourcedata, predicting the occurrence of one or more holes in a future timeperiod; generating hole prediction data; using the hole prediction data,synthesizing one or more wireless communication channels from the one ormore predicted holes; generating channel synthesis data; receiving datareflecting feedback from a previous wireless communication attempt anddata reflecting a network condition; according to the received data andthe channel synthesis data, selecting a particular wirelesscommunication channel from the one or more synthesized wirelesscommunication channels; generating wireless communication channelselection data; using the wireless communication channel selection data,instructing a radio unit to communicate using the selected wirelesscommunication channel; and instructing the radio unit to discontinue useof the selected wireless communication channel after the communicationhas been completed.

U.S. Pat. No. 10,477,342 for Systems and methods of using wirelesslocation, context, and/or one or more communication networks formonitoring for, preempting, and/or mitigating pre-identified behavior byinventor Williams, filed Dec. 13, 2017 and issued Nov. 12, 2019, isdirected to systems and methods of using location, context, and/or oneor more communication networks for monitoring for, preempting, and/ormitigating pre-identified behavior. For example, exemplary embodimentsdisclosed herein may include involuntarily, automatically, and/orwirelessly monitoring/mitigating undesirable behavior (e.g., addictionrelated undesirable behavior, etc.) of a person (e.g., an addict, aparolee, a user of a system, etc.). In an exemplary embodiment, a systemgenerally includes a plurality of devices and/or sensors configured todetermine, through one or more communications networks, a location of aperson and/or a context of the person at the location; predict andevaluate a risk of a pre-identified behavior by the person in relationto the location and/or the context; and facilitate one or more actionsand/or activities to mitigate the risk of the pre-identified behavior,if any, and/or react to the pre-identified behavior, if any, by theperson.

SUMMARY OF THE INVENTION

The present invention relates to spectrum analysis and management forelectromagnetic signals, and more particularly for providing dynamic,prioritized spectrum utilization management. Furthermore, the presentinvention relates to spectrum analysis and management forelectromagnetic (e.g., radio frequency (RF)) signals, and forautomatically identifying baseline data and changes in state for signalsfrom a multiplicity of devices in a wireless communications spectrum,and for providing remote access to measured and analyzed data through avirtualized computing network. In an embodiment, signals and theparameters of the signals are identified and indications of availablefrequencies are presented to a user. In another embodiment, theprotocols of signals are also identified. In a further embodiment, themodulation of signals, data types carried by the signals, and estimatedsignal origins are identified.

It is an object of this invention to prioritize and manage applicationsin the wireless communications spectrum, while also optimizingapplication performance.

In one embodiment, the present invention provides a system for spectrumanalysis in an electromagnetic environment including at least onemonitoring sensor including at least one receiver channel operable tomonitor the electromagnetic environment and create measured data basedon the electromagnetic environment, a radio receiver front-end subsystemconfigured to process the measured data, thereby creating processeddata, a frequency domain programmable channelizer configured to analyzethe processed data, an in-phase and quadrature (I/Q) buffer, a blinddetection engine, and a noise floor estimator, wherein the frequencydomain programmable channelizer includes buffer services, pre-processingof fast Fourier transform (FFT) bin samples, bin selection, at least oneband pass filter (BPF), an inverse fast Fourier transform (IFFT)function to produce at least one IFFT, decomposition, and/or frequencydown conversion and phase correction.

In another embodiment, the present invention provides a system forspectrum analysis in an electromagnetic environment including at leastone monitoring sensor including at least one receiver channel operableto monitor the electromagnetic environment and create measured databased on the electromagnetic environment, a radio receiver front-endsubsystem configured to process the measured data, thereby creatingprocessed data, a frequency domain programmable channelizer configuredto analyze the processed data, an in-phase and quadrature (I/Q) buffer,a blind detection engine, and a noise floor estimator, wherein thefrequency domain programmable channelizer includes buffer services,pre-processing of the FFT bin samples, bin selection, at least one bandpass filter (BPF), an inverse fast Fourier transform (IFFT) function,decomposition, and/or frequency down conversion and phase correction,and wherein the frequency domain programmable channelizer furtherincludes at least one channel definition, at least one channelizationvector, at least one FFT configuration, at least one deference matrix,at least one detector configuration, and/or at least one channeldetection.

In yet another embodiment, the present invention provides a system forspectrum analysis in an electromagnetic environment including at leastone monitoring sensor including at least one receiver channel operableto monitor the electromagnetic environment and create measured databased on the electromagnetic environment, a radio receiver front-endsubsystem configured to process the measured data, thereby creatingprocessed data, a frequency domain programmable channelizer configuredto analyze the processed data, an in-phase and quadrature (I/Q) buffer,a blind detection engine, a noise floor estimator, and a classificationengine, wherein the frequency domain programmable channelizer includesbuffer services, pre-processing of the FFT bin samples, bin selection,at least one band pass filter (BPF), an inverse fast Fourier transform(IFFT) function, decomposition, and/or frequency down conversion andphase correction, wherein the frequency domain programmable channelizerfurther includes at least one channel definition, at least onechannelization vector, at least one FFT configuration, at least onedeference matrix, at least one detector configuration, and/or at leastone channel detection, and wherein the classification engine is operableto generate a query to a static database to classify at least one signalof interest based on information from the frequency domain programmablechannelizer.

These and other aspects of the present invention will become apparent tothose skilled in the art after a reading of the following description ofthe preferred embodiment when considered with the drawings, as theysupport the claimed invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1 illustrates one embodiment of an RF awareness and analysissystem.

FIG. 2 illustrates another embodiment of the RF awareness and analysissystem.

FIG. 3 is a flow diagram of the system according to one embodiment.

FIG. 4 illustrates the acquisition component of the system.

FIG. 5 illustrates one embodiment of an analog front end of the system.

FIG. 6 illustrates one embodiment of a radio receiver front-endsubsystem.

FIG. 7 continues the embodiment of the radio receiver front-end shown inFIG. 6 .

FIG. 8 is an example of a time domain programmable channelizer.

FIG. 9 is an example of a frequency domain programmable channelizer.

FIG. 10 is another embodiment of a programmable channelizer.

FIG. 11 illustrates one embodiment of a blind detection engine.

FIG. 12 illustrates an example of an edge detection algorithm.

FIG. 13 illustrates an example of a blind classification engine.

FIG. 14 illustrates details on selection match based on cumulants formodulation selection.

FIG. 15 illustrates a flow diagram according to one embodiment of thepresent invention.

FIG. 16 illustrates control panel functions according to one embodiment.

FIG. 17 illustrates one embodiment of an RF analysis sub-architecture ofthe system.

FIG. 18 illustrates one embodiment of a detection engine of the system.

FIG. 19 illustrates a mask according to one embodiment of the presentinvention.

FIG. 20 illustrates a workflow of automatic signal detection accordingto one embodiment of the present invention.

FIG. 21 illustrates components of a Dynamic Spectrum Utilization andSharing model according to one embodiment of the present invention.

FIG. 22 illustrates a Results model provided by the system according toone embodiment of the present invention.

FIG. 23 is a table listing problems that are operable to be solved usingthe present invention.

FIG. 24 illustrates a passive geolocation radio engine system viewaccording to one embodiment of the present invention.

FIG. 25 illustrates one embodiment of an algorithm to select ageolocation method.

FIG. 26 is a diagram describing three pillars of a customer missionsolution.

FIG. 27 is a block diagram of one example of a spectrum management tool.

FIG. 28 is a block diagram of one embodiment of a resource brokerageapplication.

FIG. 29 illustrates another example of a system diagram including anautomated semantic engine and translator.

FIG. 30 illustrates a flow diagram of a method to obtain actionable databased on customer goals.

FIG. 31 illustrates a flow diagram of a method of implementation ofactionable data and knowledge decision gates from total signal flow.

FIG. 32 illustrates a flow diagram of a method to identify knowledgedecision gates based on operational knowledge.

FIG. 33 illustrates an overview of one example of information used toprovide knowledge.

FIG. 34 is a map showing locations of three macrosites, 3 SigBASE units,and a plurality of locations evaluated for alternate or additional sitedeployment for a first example.

FIG. 35 is a graph of distribution of users by average downlink PhysicalResource Block (PRB) allocation for the first example.

FIG. 36 illustrates rate of overutilization events and degree ofoverutilization for the first example.

FIG. 37A is a sector coverage map for three macrosites for the firstexample.

FIG. 37B illustrates signal strength for the sector shown in FIG. 37Afor the first example.

FIG. 37C illustrates subscriber density for the sector shown in FIG. 37Afor the first example.

FIG. 37D illustrates carrier-to-interference ratio for the sector shownin FIG. 37A for the first example.

FIG. 38A illustrates the baseline scenario shown in FIG. 34 for thefirst example.

FIG. 38B is a map showing locations of the three original macrosites andtwo additional macrosites for the first example.

FIG. 39 illustrates signal strength of the baseline scenario from FIG.38A on the left and the scenario with two additional macrosites fromFIG. 38B on the right for the first example.

FIG. 40A illustrates carrier-to-interference ratio of the baselinescenario from FIG. 38A for the first example.

FIG. 40B illustrates carrier-to-interference ratio of the scenario withtwo additional macrosites for the first example.

FIG. 41 illustrates a baseline scenario for a second example on the leftand a map showing locations of the original macrosites from the baselinescenario with three additional proposed macrosites for the secondexample on the right.

FIG. 42 illustrates signal strength of the baseline scenario from FIG.41 on the left and the scenario with three additional proposedmacrosites from FIG. 41 on the right for the second example.

FIG. 43 illustrates carrier-to-interference ratio of the baselinescenario from FIG. 41 for the second example on the left andcarrier-to-interference ratio of the scenario with three additionalproposed macrosites from FIG. 41 on the right for the second example.

FIG. 44 illustrates a signal strength comparison of a first carrier(“Carrier 1”) with a second carrier (“Carrier 2”) for 700 MHz for athird example.

FIG. 45 illustrates carrier-to-interference ratio for Carrier 1 andCarrier 2 for the third example.

FIG. 46 is a graph of Area vs. RSSI and Traffic vs. RSSI for Carrier 1and Carrier 2 for the third example.

FIG. 47 is a graph of traffic difference for Carrier 1 versus Carrier 2for the third example.

FIG. 48 is a graph of SNR vs. RSRP for each SigBASE for the thirdexample.

FIG. 49 is another graph of SNR vs. RSRP for each SigBASE for the thirdexample.

FIG. 50 is a clustered graph of SNR vs. RSRP for each SigBASE for thethird example.

FIG. 51 is another clustered graph of SNR vs. RSRP for each SigBASE forthe third example.

FIG. 52 is a schematic diagram of a system of the present invention.

FIG. 53 illustrates one embodiment of a flow diagram of a channelizerconfiguration.

FIG. 54 illustrates another embodiment of a flow diagram of achannelizer.

FIG. 55 illustrates yet another embodiment of a flow diagram of achannelizer configuration.

FIG. 56A illustrates one embodiment of probability density functions perbin for noise and a signal with the noise.

FIG. 56B illustrates an example of a plurality of frequency bins and acritical value or threshold.

FIG. 56C illustrates another example of a plurality of frequency binsand a critical value or threshold.

FIG. 57A illustrates one example of probability on the channel level.

FIG. 57B illustrates one example of probability on the bin level.

FIG. 58A illustrates an example showing a critical value (γ), powerlevels of a noise signal, and power levels of a signal with noise.

FIG. 58B illustrates an example of the probability of false alarm for anoise signal.

FIG. 58C illustrates an example of the probability of missed detectionfor a signal with noise.

FIG. 59 illustrates one example of probabilities.

FIG. 60 illustrates an example of equations for hypothesis testing withchannels for the following scenarios: (1) Noise Only (No Signal), AssertNoise Only, (2) Noise Only (No Signal), Assert Noise and Signal, (3)Noise and Signal, Assert Noise Only, and (4) Noise and Signal, AssertNoise and Signal.

FIG. 61 illustrates one example of an algorithm used in a channelizer.

FIG. 62A illustrates one example of a simulated fast Fourier transform(FFT), noise floor estimation, and comparison with channelizationvectors.

FIG. 62B illustrates channelization vectors and comparisons for the FFTframe shown in FIG. 62A.

FIG. 62C illustrates amplitude probability distribution for the FFTframe shown in FIG. 62A.

FIG. 62D illustrates FFT frames grouped by block for the FFT frame shownin FIG. 62A.

FIG. 62E illustrates average power FFT by block for the FFT frame shownin FIG. 62A.

FIG. 62F illustrates the channelization vectors for the FFT frame shownin FIG. 62A. The x-axis indicates the frequency of the bin.

FIG. 62G illustrates the comparison vectors for the FFT frame shown inFIG. 62A.

FIG. 63A illustrates one embodiment of preemption by wideband detection.

FIG. 63B illustrates the example shown in FIG. 63A accounting forwideband detection.

FIG. 64 illustrates one embodiment of maximum resolution bandwidth (RBW)for vector resolution.

FIG. 65A illustrates one example of a spectrum scenario.

FIG. 65B illustrates an embodiment of channelization vectors.

FIG. 66A illustrates one example of bandwidth selection.

FIG. 66B illustrates an example using the bandwidth selection in FIG.66A.

FIG. 67A illustrates another example of an FFT frame illustrating thecenter frequency on the x-axis and the frequency bin mean power level(Fbpower_mean) on the y-axis.

FIG. 67B illustrates channelization vectors and comparisons for the FFTframe shown in FIG. 67A.

FIG. 67C illustrates a graph for the FFT frame shown in FIG. 67A.

FIG. 67D illustrates a table for the FFT frame shown in FIG. 67A.

FIG. 68A illustrates an example of spectrum by channel.

FIG. 68B illustrates an example of channel detection for the spectrumshown in FIG. 68A.

FIG. 68C illustrates an example of a cascade for the spectrum shown inFIG. 68A.

FIG. 69A illustrates an example of a graph of total signal power.

FIG. 69B illustrates an example of a graph of total noise power.

FIG. 70A illustrates another example of a simulated FFT, noise floorestimation, and comparison with channelization vectors.

FIG. 70B illustrates channelization vectors and comparisons for the FFTframe shown in FIG. 70A.

FIG. 70C illustrates channelization vectors and comparisons for the FFTframe shown in FIG. 70A.

DETAILED DESCRIPTION

The present invention is generally directed to spectrum analysis andmanagement for electromagnetic signals, and more particularly forproviding dynamic, prioritized spectrum utilization management.

In one embodiment, the present invention provides a system for spectrumanalysis in an electromagnetic environment including at least onemonitoring sensor including at least one receiver channel operable tomonitor the electromagnetic environment and create measured data basedon the electromagnetic environment, a radio receiver front-end subsystemconfigured to process the measured data, thereby creating processeddata, a frequency domain programmable channelizer configured to analyzethe processed data, an in-phase and quadrature (I/Q) buffer, a blinddetection engine, and a noise floor estimator, wherein the frequencydomain programmable channelizer includes buffer services, pre-processingof fast Fourier transform (FFT) bin samples, bin selection, at least oneband pass filter (BPF), an inverse fast Fourier transform (IFFT)function to produce at least one IFFT, decomposition, and/or frequencydown conversion and phase correction. In one embodiment, one or more ofthe at least one monitoring sensor is mounted on a drone, on a vehicle,in or on a street light, in or on a traffic pole, and/or on top of abuilding. In one embodiment, the frequency domain programmablechannelizer includes a comparison at each of the at least one receiverchannel, and wherein the comparison provides anomalous detection using amask with frequency and power. In one embodiment, the frequency domainprogrammable channelizer includes channelization selector logic for atable lookup of filter coefficient and channelization vectors. In oneembodiment, data from the table lookup of filter coefficient andchannelization vectors undergoes preprocessing with a mix circularrotator to produce a plurality of blocks of a plurality of points. Inone embodiment, a sum is taken of a block of the plurality of blocks andthe at least one IFFT is taken of a point of the plurality of points toproduce discor overlap samples, and wherein the discor overlap samplesare transmitted to a classification engine. In one embodiment, data fromthe frequency domain programmable channelizer undergoes an N point FFT,wherein a power spectral density (PSD) is calculated for the N pointFFT, wherein a complex average FFT is obtained for a plurality of blocksof the N point FFT. In one embodiment, the PSD is transmitted to thenoise floor estimator. In one embodiment, the frequency domainprogrammable channelizer includes at least one channel definition, atleast one channelization vector, at least one FFT configuration, atleast one deference matrix, at least one detector configuration, and/orat least one channel detection. In one embodiment, the noise floorestimator is operable to estimate a bin-wise noise model, estimate abin-wise noise plus signal model, determine a bin-level probability offalse alarm, a bin-level threshold, a channel-level probability of falsealarm, a channel-level level threshold, calculate a detection vector,count a number of elements above the bin-level threshold, determine aprobability of false alarm, determine a probability of missed detection,and/or determine an overall detection probability. In one embodiment,the blind detection engine is operable to estimate a number of channels,corresponding bandwidths for the number of channels, and centerfrequencies using an averaged power spectral density (PSD) of at leastone signal of interest. In one embodiment, the system further includes aclassification engine, wherein the classification engine is operable togenerate a query to a static database to classify at least one signal ofinterest based on information from the frequency domain programmablechannelizer.

In another embodiment, the present invention provides a system forspectrum analysis in an electromagnetic environment including at leastone monitoring sensor including at least one receiver channel operableto monitor the electromagnetic environment and create measured databased on the electromagnetic environment, a radio receiver front-endsubsystem configured to process the measured data, thereby creatingprocessed data, a frequency domain programmable channelizer configuredto analyze the processed data, an in-phase and quadrature (I/Q) buffer,a blind detection engine, and a noise floor estimator, wherein thefrequency domain programmable channelizer includes buffer services,pre-processing of the FFT bin samples, bin selection, at least one bandpass filter (BPF), an inverse fast Fourier transform (IFFT) function,decomposition, and/or frequency down conversion and phase correction,and wherein the frequency domain programmable channelizer furtherincludes at least one channel definition, at least one channelizationvector, at least one FFT configuration, at least one deference matrix,at least one detector configuration, and/or at least one channeldetection. In one embodiment, the at least one deference matrix isoperable to identify at least one narrowband channel that is a subset ofat least one wideband channel. In one embodiment, the at least one FFTconfiguration is operable to resolve ambiguities between at least twochannels by employing a sufficient resolution bandwidth. In oneembodiment, the at least one channelization vector is operable tospecify normalized power levels per FFT bin for at least one channel. Inone embodiment, power levels of the at least one channelization vectorare normalized with respect to peak power in a spectrum envelope of atleast one channel. In one embodiment, the at least one detectorconfiguration includes a minimum acceptable probability of false alarmand/or a minimum acceptable probability of missed detection. In oneembodiment, the at least one channel detection is operable to perform ahypothesis test for at least one bin using information from the noisefloor estimator and a maximum probability of false alarm.

In yet another embodiment, the present invention provides a system forspectrum analysis in an electromagnetic environment including at leastone monitoring sensor including at least one receiver channel operableto monitor the electromagnetic environment and create measured databased on the electromagnetic environment, a radio receiver front-endsubsystem configured to process the measured data, thereby creatingprocessed data, a frequency domain programmable channelizer configuredto analyze the processed data, an in-phase and quadrature (I/Q) buffer,a blind detection engine, a noise floor estimator, and a classificationengine, wherein the frequency domain programmable channelizer includesbuffer services, pre-processing of the FFT bin samples, bin selection,at least one band pass filter (BPF), an inverse fast Fourier transform(IFFT) function, decomposition, and/or frequency down conversion andphase correction, wherein the frequency domain programmable channelizerfurther includes at least one channel definition, at least onechannelization vector, at least one FFT configuration, at least onedeference matrix, at least one detector configuration, and/or at leastone channel detection, and wherein the classification engine is operableto generate a query to a static database to classify at least one signalof interest based on information from the frequency domain programmablechannelizer.

Traditional management of spectrum is static, based on licenses that aregeographical and band specific. The Federal Communications Commission(FCC) has allocated spectrum into a table. Utilization is increased byslicing the spectrum into finer slices. Additionally, interference islimited by imposing penalties by strict geographical band utilizationrules and licenses. However, these traditional methods of spectrummanagement do not work with increasing demand and new services comingout. The new services would have to be at higher frequencies (e.g.,above 10 GHz), which is very expensive and requires costly transceiverwith a limited distance range.

Spectrum is valuable because it is a finite resource. Further, thedemand for spectrum is ever-increasing. The Shannon-Hartley theoremcalculates the maximum rate at which information can be transmitted overa communications channel of a specified bandwidth in the presence ofnoise as follows:C=BW log₂(1+SNR)where C is the channel capacity in bits per second, BW is the bandwidthof the channel in Hz, and SNR is the signal-to-noise ratio.

Early attempts at managing spectrum include developing technology thatincreases spectrum efficiency (i.e., maximizing SNR). Although thisresults in more bits per Hz, the logarithmic function limits the gainsin channel capacity resulting from improving technology. Additionalattempts at managing spectrum also include developing technology toenable use of alternate spectrum (e.g., free-space optical (FSO)communication). However, using alternate spectrum, such as higherfrequencies, leads to smaller ranges, line of sight limitations,increased elevation of transmission structures, and/or expensiveinfrastructure.

The missing component to spectrum management is bandwidth management.Bandwidth management provides flexible utilization of the spectrum,enables management of spectrum resources and users, while allowingspectrum usage to be quantified. The majority of applications using thespectrum can coexist if each application knows about the spectrum needsof other applications and how they plan to use the spectrum. However,because the needs of each application are dynamic, a dynamic spectrummanagement system is needed. The present invention allows autonomous,dynamic sharing of the electromagnetic spectrum to allow maximumutilization by diverse applications according to specific utilizationrules (dynamic and/or static) while maintaining minimum interferencebetween applications. This requires new tools that provide dynamicenvironmental spectral awareness of all signals present in theelectromagnetic (e.g., radio frequency (RF)) environment to properlyexecute utilization rules, which are operable to describe or facilitatesharing spectrum resources among several competing users or protect oneservice user from others, among others.

5G requires spectrum awareness. Larger blocks of spectrum are requiredto support higher speeds. Dynamic spectrum sharing is necessary to makethe spectrum assets available. Further, visibility of spectrum activityis required to support reliability targets. Interference avoidance andresolution must be embedded. Internet of Things (IoT)/machinecommunication wireless dependency elevates the need for real-time RFvisibility to avoid disruption and safety concerns.

The system of the present invention provides scalable processingcapabilities at the edge. Edge processing is fast and reliable with lowlatency. Environmental sensing processes optimize collection andanalytics, making data sets manageable. Advantageously, the systemminimizes backhaul requirements, allowing for actionable data to bedelivered faster and more efficiently.

Deep learning techniques extract and deliver knowledge from large datasets in near-real time. These deep learning techniques are critical foridentifying and classifying signals. Edge analytics further allow thirdparty data (e.g., social media, population information, real estateinformation, traffic information, geographic information system) tofurther enrich captured data sets. A semantic engine and inferencereasoner leverages insights generated by machine learning and edgeanalytics. Ontologies are established allowing for the creation ofknowledge operable to inform and direct actions and/or decisions.

Referring now to the drawings in general, the illustrations are for thepurpose of describing one or more preferred embodiments of the inventionand are not intended to limit the invention thereto.

The present invention provides systems, methods, and apparatuses forspectrum analysis and management by identifying, classifying, andcataloging at least one or a multiplicity of signals of interest basedon electromagnetic spectrum measurements (e.g., radiofrequency spectrummeasurements), location, and other measurements. The present inventionuses real-time and/or near real-time processing of signals (e.g.,parallel processing) and corresponding signal parameters and/orcharacteristics in the context of historical, static, and/or statisticaldata for a given spectrum, and more particularly, all using baselinedata and changes in state for compressed data to enable near real-timeanalytics and results for individual monitoring sensors and foraggregated monitoring sensors for making unique comparisons of data.

The systems, methods, and apparatuses according to the present inventionpreferably are operable to detect in near real time, and more preferablyto detect, sense, measure, and/or analyze in near real time, and morepreferably to perform any near real time operations within about 1second or less. In one embodiment, near real time is defined ascomputations completed before data marking an event change. For example,if an event happens every second, near real time is completingcomputations in less than one second. Advantageously, the presentinvention and its real time functionality described herein uniquelyprovide and enable the system to compare acquired spectrum data tohistorical data, to update data and/or information, and/or to providemore data and/or information on open space. In one embodiment,information (e.g., open space) is provided on an apparatus unit or adevice that is occupying the open space. In another embodiment, thesystem compares data acquired with historically scanned (e.g., 15 min to30 days) data and/or or historical database information in near-realtime. Also, the data from each monitoring sensor, apparatus unit, ordevice and/or aggregated data from more than one monitoring sensor,apparatus unit, and/or device are communicated via a network to at leastone server computer and stored on a database in a virtualized orcloud-based computing system, and the data is available for secure,remote access via the network from distributed remote devices havingsoftware applications (apps) operable thereon, for example by web access(mobile app) or computer access (desktop app). The at least one servercomputer is operable to analyze the data and/or the aggregated data.

The system is operable to monitor the electromagnetic (e.g., RF)environment via at least one monitoring sensor. The system is thenoperable to analyze data acquired from the at least one monitoringsensor to detect, classify, and/or identify at least one signal in theelectromagnetic environment. The system is operable to learn theelectromagnetic environment, which allows the system to extractenvironmental awareness. In a preferred embodiment, the system extractsenvironmental awareness by including customer goals. The environmentalawareness is combined with the customer goals, customer definedpolicies, and/or rules (e.g., customer defined rules, government definedrules) to extract actionable information to help the customer optimizeperformance according to the customer goals. The actionable informationis combined and correlated with additional information sources toenhance customer knowledge and user experience through dynamic spectrumutilization and prediction models.

The systems, methods, and apparatuses of the various embodiments enablespectrum utilization management by identifying, classifying, andcataloging signals of interest based on electromagnetic (e.g., radiofrequency) measurements. In one embodiment, signals and parameters ofthe signals are identified. In another embodiment, indications ofavailable frequencies are presented to a user and/or user equipment. Inyet another embodiment, protocols of signals are also identified. In afurther embodiment, the modulation of signals, data types carried by thesignals, and estimated signal origins are identified. Identification,classification, and cataloging signals of interest preferably occurs inreal time or near-real time.

Embodiments are directed to a spectrum monitoring unit that isconfigurable to obtain spectrum data over a wide range of wirelesscommunication protocols. Embodiments also provide for the ability toacquire data from and send data to database depositories that are usedby a plurality of spectrum management customers and/or applications orservices requiring spectrum resources.

In one embodiment, the system includes at least one spectrum monitoringunit. Each of the at least one spectrum monitoring unit includes atleast one monitoring sensor that is preferably in network communicationwith a database system and spectrum management interface. In oneembodiment, the at least one spectrum monitoring unit and/or the atleast one monitoring sensor is portable. In a preferred embodiment, oneor more of the at least one spectrum monitoring unit and/or the at leastone monitoring sensor is a stationary installation. The at least onespectrum monitoring unit and/or the at least one monitoring sensor isoperable to acquire different spectrum information including, but notlimited to, frequency, bandwidth, signal power, time, and location ofsignal propagation, as well as modulation type and format. The at leastone spectrum monitoring unit is preferably operable to provide signalidentification, classification, and/or geo-location. Additionally, theat least one spectrum monitoring unit preferably includes a processor toallow the at least one spectrum monitoring unit to process spectrumpower density data as received and/or to process raw In-Phase andQuadrature (I/Q) complex data. Alternatively, the at least one spectrummonitoring unit and/or the at least one monitoring sensor transmits thedata to at least one data analysis engine for storage and/or processing.In a preferred embodiment, the transmission of the data is via abackhaul operation. The spectrum power density data and/or the raw I/Qcomplex data are operable to be used to further signal processing,signal identification, and data extraction.

The system preferably is operable to manage and prioritize spectrumutilization based on five factors: frequency, time, spatial, signalspace, and application goals.

The frequency range is preferably as large as possible. In oneembodiment, the system supports a frequency range between 1 MHz and 6GHz. In another embodiment, the system supports a frequency range with alower limit of 9 kHz. In yet another embodiment, the system supports afrequency range with a higher limit of 12.4 GHz. In another embodiment,the system supports a frequency range with a higher limit of 28 GHz or36 GHz. Alternatively, the system supports a frequency range with ahigher limit of 60 GHz. In still another embodiment, the system supportsa frequency range with a higher limit of 100 GHz. The system preferablyhas an instantaneous processing bandwidth (IPBW) of 40 MHz, 80 MHz, 100MHz, or 250 MHz per channel.

The time range is preferably as large as possible. In one embodiment,the number of samples per dwell time in a frequency band is calculated.In one example, the system provides a minimum coverage of 2 seconds. Thenumber of samples per dwell in time in the frequency band is calculatedas follows:N _(s)≥(IPBW)(2)/channelThe storage required in a buffer is a minimum of 2 seconds per channelper dwell time, which is calculated as follows:storage=(IPBW)(2)(2Bytes)(channels)/(dwell time)

Spatial processing is used to divide an area of coverage by a range ofazimuth and elevation angles. The area of coverage is defined as an areaunder a certain azimuth and range. This is implemented by antenna arraysprocessing, steerable beamforming, array processing, and/or directionalantennas. In one embodiment, the directional antennas include at leastone steerable electrical or mechanical antenna. Alternatively, thedirectional antennas include an array of steerable antennas. Moreantennas require more signal processing. Advantageously, spatialprocessing allows for better separation of signals, reduction of noiseand interference signals, geospatial separation, increasing signalprocessing gains, and provides a spatial component to signalidentification. Further, this allows for simple integration ofgeolocation techniques, such as time difference of arrival (TDOA), angleof arrival (AOA), and/or frequency difference of arrival (FDOA). Thisalso allows for implementation of a geolocation engine, which will bediscussed in detail infra.

Each signal has inherent signal characteristics including, but notlimited to a modulation type (e.g., frequency modulation (FM), amplitudemodulation (AM), quadrature phase-shift keying (QPSK), quadratureamplitude modulation (QAM), binary phase-shift keying (BPSK), etc.), aprotocol used (e.g., no protocol for analog signals, digital mobileradio (DMR), land mobile radio (LMR), Project 25 (P25), NXDN, cellular,long-term evolution (LTE), universal mobile telecommunications system(UMTS), 5G), an envelope behavior (e.g., bandwidth (BW), centerfrequency (Fc), symbol rate, data rate, constant envelope, peak power toaverage power ratio (PAR), cyclostationary properties), an interferenceindex, and statistical properties (e.g., stationary, cyclostationary,higher moment decomposition, non-linear decomposition (e.g., Volterraseries to cover non-linearities, learning basic model).

The application goals are dependent on the particular application usedwithin the system. Examples of applications used in the system include,but are not limited to, traffic management, telemedicine, virtualreality, streaming video for entertainment, social media, autonomousand/or unmanned transportation, etc. Each application is operable to beprioritized within the system according to customer goals. For example,traffic management is a higher priority application than streaming videofor entertainment.

As previously described, the system is operable to monitor theelectromagnetic (e.g., RF) environment, analyze the electromagneticenvironment, and extract environmental awareness of the electromagneticenvironment. In a preferred embodiment, the system extracts theenvironmental awareness of the electromagnetic environment by includingcustomer goals. In another embodiment, the system uses the environmentalawareness with the customer goals and/or user defined policies and rulesto extract actionable information to help the customer optimize thecustomer goals. The system combines and correlates other informationsources with the extracted actionable information to enhance customerknowledge through dynamic spectrum utilization and prediction models.

FIG. 1 illustrates one embodiment of an RF awareness and analysissystem. The system includes an RF awareness subsystem. The RF awarenesssubsystem includes, but is not limited to, an antenna subsystem, an RFconditioning subsystem, at least one front end receiver, a programmablechannelizer, a blind detection engine, a blind classification engine, anenvelope feature extraction module, a demodulation bank, an automaticgain control (AGC) double loop subsystem, a signal identificationengine, a feature extraction engine, a learning engine, a geolocationengine, a data analysis engine, and/or a database storing informationrelated to at least one signal (e.g., metadata, timestamps, powermeasurements, frequencies, etc.). The system further includes an alarmsystem, a visualization subsystem, a knowledge engine, an operationalsemantic engine, a customer optimization module, a database of customergoals and operational knowledge, and/or a database of actionable dataand decisions.

The antenna subsystem monitors the electromagnetic (e.g., RF)environment to produce monitoring data. The monitoring data is thenprocessed through the RF conditioning subsystem before being processedthrough the front end receivers. The AGC double loop subsystem isoperable to perform AGC adjustment. Data is converted from analog todigital by the front end receivers.

The digital data is then sent through the programmable channelizer, andundergoes I,Q buffering and masking. A fast Fourier transform (FFT) isperformed and the blind detection engine performs blind detection.Additionally, the blind classification engine performs blindclassification. Information (e.g., observed channels) is shared from theblind detection engine to the blind classification and/or theprogrammable channelizer (e.g., to inform logic and selectionprocesses). Information from the blind detection engine is also sent tothe envelope feature extraction module. Information from the blindclassification engine is sent to the demodulation bank.

Information from the envelope feature extraction module, thedemodulation bank, and/or the blind classification engine are operableto be used by the signal identification engine, the feature extractionengine, the learning engine, and/or the geolocation engine. Informationfrom the AGC double loop subsystem, the I,Q buffer, masking, theprogrammable channelizer, the signal identification engine, the featureextraction engine, the learning engine, and the geolocation engine, theenvelope feature extraction module, the demodulation bank, and/or theblind classification engine is operable to be stored in the databasestoring information related to the at least one signal (e.g., signaldata, metadata, timestamps).

Information from the database (i.e., the database storing informationrelated to the at least one signal), the signal identification engine,the feature extraction engine, the learning engine, and/or thegeolocation engine is operable to be sent to the data analysis enginefor further processing.

The alarm system includes information from the database storinginformation related to the at least one signal and/or the database ofcustomer goals and operational knowledge. Alarms are sent from the alarmsystem to the visualization subsystem. In a preferred embodiment, thevisualization subsystem customizes a graphical user interface (GUI) foreach customer. The visualization system is operable to displayinformation from the database of actionable data and decisions. In oneembodiment, the alarms are sent via text message and/or electronic mail.In one embodiment, the alarms are sent to at least one internet protocol(IP) address.

The database of customer goals and operational knowledge is alsooperable to send information to a semantic engine (e.g., customer alarmconditions and goals) and/or an operational semantic engine (e.g.,customer operational knowledge). The semantic engine translatesinformation into constraints and sends the constraints to the customeroptimization module, which also receives information (e.g., signalmetadata) from the data analysis engine. The customer optimizationmodule is operable to send actionable data related to theelectromagnetic environment to the operational semantic engine. Thecustomer optimization module is operable to discern which information(e.g., environmental information) has the largest statisticallysufficient impact related to the customer goals and operation.

In one embodiment, the system includes at least one monitoring sensor,at least one data analysis engine, at least one application, a semanticengine, a programmable rules and policy editor, a tip and cue server,and/or a control panel as shown in FIG. 2 .

The at least one monitoring sensor includes at least one radio serverand/or at least one antenna. The at least one antenna is a singleantenna (e.g., uni-directional or directional) or an antenna arrayformed of multiple antennas resonating at different frequency bands andconfigured in a 1D (linear), 2D (planar), or 3D (area) antennaconfiguration. The at least one monitoring sensor is operable to scanthe electromagnetic (e.g., RF) spectrum and measure properties of theelectromagnetic spectrum, including, but not limited to, receiver I/Qdata. The at least one monitoring unit is preferably operable toautonomously capture the electromagnetic spectrum with respect tofrequency, time, and/or space. In one embodiment, the at least onemonitoring sensor is operable to perform array processing.

In another embodiment, the at least one monitoring sensor is mobile. Inone embodiment, the at least one monitoring sensor is mounted on avehicle or a drone. Alternatively, the at least one monitoring sensor isfixed. In one embodiment, the at least one monitoring sensor is fixed inor on a street light and/or a traffic pole. In yet another embodiment,the at least one monitoring sensor is fixed on top of a building.

In one embodiment, the at least one monitoring sensor is integrated withat least one camera. In one embodiment, the at least one camera capturesvideo and/or still images.

In another embodiment, the at least one monitoring sensor includes atleast one monitoring unit. Examples of monitoring units include thosedisclosed in U.S. Pat. Nos. 10,122,479, 10,219,163, 10,231,206,10,237,770, 10,244,504, 10,257,727, 10,257,728, 10,257,729, 10,271,233,10,299,149, 10,498,951, and 10,529,241, and U.S. Publication Nos.20190215201, 20190364533, and 20200066132, each of which is incorporatedherein by reference in its entirety.

In a preferred embodiment, the system includes at least one dataanalysis engine to process data captured by the at least one monitoringsensor. An engine is a collection of functions and algorithms used tosolve a class of problems. The system preferably includes a detectionengine, a classification engine, an identification engine, ageo-location engine, a learning engine, and/or a statistical inferenceand machine learning engine. For example, the geolocation engine is agroup of functions and geolocation algorithms that are used together tosolve multiple geolocation problems.

The detection engine is preferably operable to detect at least onesignal of interest in the electromagnetic (e.g., RF) environment. In apreferred embodiment, the detection engine is operable to automaticallydetect the at least one signal of interest. In one embodiment, theautomatic signal detection process includes mask creation andenvironment analysis using masks. Mask creation is a process ofelaborating a representation of the electromagnetic environment byanalyzing a spectrum of signals over a certain period of time. A desiredfrequency range is used to create a mask, and FFT streaming data is alsoused in the mask creation process. A first derivative is calculated andused for identifying possible maximum power values. A second derivativeis calculated and used to confirm the maximum power values. A movingaverage value is created as FFT data is received during a time periodselected by the user for mask creation. For example, the time period is10 seconds. The result is an FFT array with an average of the maximumpower values, which is called a mask.

The classification engine is preferably operable to classify the atleast one signal of interest. In one embodiment, the classificationengine generates a query to a static database to classify the at leastone signal of interest based on its components. For example, theinformation stored in static database is preferably used to determinespectral density, center frequency, bandwidth, baud rate, modulationtype, protocol (e.g., global system for mobile (GSM), code-divisionmultiple access (CDMA), orthogonal frequency-division multiplexing(OFDM), LTE, etc.), system or carrier using licensed spectrum, locationof the signal source, and/or a timestamp of the at least one signal ofinterest. In an embodiment, the static database includes frequencyinformation gathered from various sources including, but not limited to,the Federal Communication Commission, the InternationalTelecommunication Union, and data from users. In one example, the staticdatabase is an SQL database. The data store is operable to be updated,downloaded or merged with other devices or with its main relationaldatabase. In one embodiment, software application programming interface(API) applications are included to allow database merging withthird-party spectrum databases that are only operable to be accessedsecurely. In a preferred embodiment, the classification engine isoperable to calculate second, third, and fourth order cumulants toclassify modulation schemes along with other parameters, includingcenter frequency, bandwidth, baud rate, etc.

The identification engine is preferably operable to identify a device oran emitter transmitting the at least one signal of interest. In oneembodiment, the identification engine uses signal profiling and/orcomparison with known database(s) and previously recorded profile(s) toidentify the device or the emitter. In another embodiment, theidentification engine states a level of confidence related to theidentification of the device or the emitter.

The geolocation engine is preferably operable to identify a locationfrom which the at least one signal of interest is emitted. In oneembodiment, the geolocation engine uses statistical approximations toremove error causes from noise, timing and power measurements,multipath, and non-line of sight (NLOS) measurements. By way of example,the following methods are used for geolocation statisticalapproximations and variances: maximum likelihood (nearest neighbor orKalman filter); least squares approximation; Bayesian filter if priorknowledge data is included; and the like. In another embodiment, timedifference of arrival (TDOA) and frequency difference of arrival (FDOA)equations are derived to assist in solving inconsistencies in distancecalculations. In still another embodiment, angle of arrival (AOA) isused to determine geolocation. In yet another embodiment, powerdistribution ratio versus azimuth measurements are used to determinegeolocation. In a preferred embodiment, geolocation is performed usingAngle of Arrival (AOA), Time Difference of Arrival (TDOA), FrequencyDifference of Arrival (FDOA), and power distribution ratio measurements.Several methods or combinations of these methods are operable to be usedwith the present invention because geolocation is performed in differentenvironments, including but not limited to indoor environments, outdoorenvironments, hybrid (stadium) environments, inner city environments,etc.

The learning engine is preferably operable to learn the electromagneticenvironment. In one embodiment, the learning engine uses statisticallearning techniques to observe and learn an electromagnetic environmentover time and identify temporal features of the electromagneticenvironment (e.g., signals) during a learning period. In a preferredembodiment, the learning engine is operable to learn information fromthe detection engine, the classification engine, the identificationengine, and/or the geolocation engine. In one embodiment, the learningfunction of the system is operable to be enabled and disabled. When thelearning engine is exposed to a stable electromagnetic environment andhas learned what is normal in the electromagnetic environment, it willstop its learning process. In a preferred embodiment, theelectromagnetic environment is periodically reevaluated. In oneembodiment, the learning engine reevaluates and/or updates theelectromagnetic environment at a predetermined timeframe. In anotherembodiment, the learning engine reevaluates and/or updates theelectromagnetic environment is updated after a problem is detected.

The statistical inference and machine learning (ML) engine utilizesstatistical learning techniques and/or control theory to learn theelectromagnetic environment and make predictions about theelectromagnetic environment.

The survey occupancy application is operable to determine occupancy infrequency bands. In another embodiment, the survey occupancy applicationis operable to schedule occupancy in a frequency band. The surveyoccupancy application is also used to preprocess at least two signalsthat exist in the same band based on interference between the at leasttwo signals.

The resource brokerage application is operable to optimize resources toimprove application performance. In a preferred embodiment, the resourcebrokerage application is operable to use processed data from the atleast one monitoring sensor and/or additional information to determineenvironmental awareness (e.g., environmental situational awareness). Theenvironmental awareness and/or capabilities of a device and/or aresource are used to determine policies and/or reasoning to optimize thedevice and/or the resource. The resource brokerage application isoperable to control the device and/or the resource. Additionally, theresource brokerage application is operable to control the at least onemonitoring sensor.

The certification and compliance application is operable to determine ifapplications and/or devices are behaving according to rules and/orpolicies (e.g., customer policies and/or rules, government rules). Inanother embodiment, the certification and compliance application isoperable to determine if the applications and/or the devices are sharingfrequency bands according to the rules and/or the policies. In yetanother embodiment, the certification and compliance application isoperable to determine if the applications and/or the devices arebehaving according to non-interferences rules and/or policies.

The sharing application is operable to determine optimization of howapplications and/or devices share the frequency bands. In a preferredembodiment, the sharing application uses a plurality of rules and/orpolicies (e.g., a plurality of customer rules and/or policies,government rules) to determine the optimization of how the applicationsand/or the devices share the frequency bands. Thus, the sharingapplication satisfies the plurality of rules and/or policies as definedby at least one customer and/or the government.

The statistical inference and prediction utilization application isoperable to utilize predictive analytics techniques including, but notlimited to, machine learning (ML), artificial intelligence (AI), neuralnetworks (NNs), historical data, and/or data mining to make futurepredictions and/or models. The system is preferably operable torecommend and/or perform actions based on historical data, external datasources, ML, AI, NNs, and/or other learning techniques.

The semantic engine is operable to receive data in forms including, butnot limited to, audio data, text data, video data, and/or image data. Inone embodiment, the semantic engine utilizes a set of system rulesand/or a set of system policies. In another embodiment, the set ofsystem rules and/or the set of system policies is created using a priorknowledge database. The semantic engine preferably includes an editorand a language dictionary.

The semantic engine preferably further includes a programmable rules andpolicy editor. The programmable rules and policy editor is operable toinclude at least one rule and/or at least one policy. In one embodiment,the at least one rule and/or the at least one policy is defined by atleast one customer. Advantageously, this allows the at least onecustomer to dictate rules and policies related to customer objectives.

The system further includes a tip and cue server. The tip and cue serveris operable utilize the environmental awareness from the data processedby the at least one data analysis engine in combination with additionalinformation to create actionable data. In a preferred embodiment, thetip and cue server utilizes information from a specific rule set (e.g.,customer defined rule set), further enhancing the optimizationcapabilities of the system. The specific rule set is translated intooptimization objectives, including constraints associated with signalcharacteristics. In a preferred embodiment, the tip and cue server isoperable to activate at least one alarm and/or provide at least onereport. In another embodiment, the tip and cue server is operable toactivate the at least one alarm and/or provide the at least one reportaccording to the specific rule set.

Advantageously, the system is operable to run autonomously andcontinuously. The system learns from the environment, and, withoutoperator intervention, is operable to detect anomalous signals thateither were not there before, or have changed in power or bandwidth.Once detected, the system is operable to send alerts (e.g., by text oremail) and begin high resolution spectrum capture, or I/Q capture of thesignal of interest. Additionally, the system is operable to optimize andprioritize applications using the learning engine.

FIG. 3 is a flow diagram of the system according to one embodiment.

FIG. 4 illustrates the acquisition component of the system. The systemincludes an antenna subsystem including at least one antenna, an analogfront-end conditioning system, a radio receiver front-end system, and aI/Q buffer. The system is operable to perform control functionsincluding, but not limited to, controlling a radio server, conditioningthe radio server, I/Q flow control and/or time stamping, and/or buffermanagement.

FIG. 5 illustrates one embodiment of an analog front end of the system.In one embodiment, electromagnetic waves are sent directly to a radioreceiver front-end subsystem as shown in Path A. Alternatively, theelectromagnetic waves are sent through an analog filter bank andamplifier/channel with a filter (SSS), an amplifier (e.g., variable gainamplifier), and an automatic gain controller as shown in Path B beforereaching the radio receiver front-end subsystem. In one embodiment, theBCU is 80 MHz. Alternatively, the BCU is 150 MHz. The radio receiverfront-end subsystem is described in FIG. 6 .

FIG. 6 illustrates one embodiment of a radio receiver front-endsubsystem. Path A and Path B continue into a radio-frequency integratedcircuit (RFIC), and then proceed to a digital down-converter (DDC)before downsampling (e.g., decimation) and moving through a fieldprogrammable gate array (FPGA). In one embodiment, signals from the FPGAare operable to be sent to a digital to analog converter (DAC).Alternatively, signals are sent via bus to a Universal Software RadioPeripheral hardware driver (UHD) host and SD controller beforecontinuing to Path E, which is described in FIG. 7 .

FIG. 7 continues the embodiment of the radio receiver front-end shown inFIG. 6 after digitization. In one embodiment, Path E continues to theI,Q buffer. In another embodiment, Path E continues to a basebandreceiver. In one embodiment, signals are further processed using signalprocessing software (e.g., GNU Radio software). In yet anotherembodiment, the baseband receiver is connected to inputs and/or outputs.In one embodiment, the inputs include, but are not limited to, MicroSDFlash memory and/or a Universal Serial Bus (USB) console. In oneembodiment, the outputs include, but are not limited to, USB 2.0 hostand/or audio. Alternatively, data from the baseband receiver is sent tothe I,Q buffer via the IGbE port.

The system preferably uses multiple receiver channels for the front end.In one embodiment, there are 4 receiver channels. Alternatively, thereare 8, 12, 16, or 32 receiver channels. I,Q data is preferably tagged bythe receiver channel and receiver antenna (e.g., bandwidth, gain, etc.)and then stored in the I,Q buffer before analysis is completed.

Advantageously, the system is hardware agnostic. The system is operableto provide a suggestion for hardware for a particular frequency set.Additionally, the hardware agnostic nature of the system allows forestablished architecture to persist. The system is cost effectivebecause it also allows for cheaper antennas to be used, as well as lessexpensive filters, because calibration can be done using the systemrather than the antennas and/or filters, as well as post-ADC processingto rectify any performance loss. Because the system processes allsignals present in the spectrum and their inter-relationships to extractenvironmental awareness, so the analog front end does not requireelaborate filtering to avoid interference and provide optimum dynamicrange. Additionally, the analog front end does not require optimalantennas for all frequency bands and ranges to obtain environmentalawareness.

For a time domain programmable channelizer, all filters' impulseresponses must be programmable and the number of filters must beprogrammable. Additionally, the channel bandwidth resolution must beprogrammable starting from a minimum bandwidth. The center frequency ofeach channel must also be programmable. Decimation is based on channelbandwidth and desired resolution. However, these requirements aredifficult to implement for channels with variable bandwidth and centerfrequency. Wavelet filters can be used effectively if the centerfrequency and channel's bandwidth follow a tree structure (e.g., Harrand Deubauchi wavelets). FIG. 8 is an example of a time domainprogrammable channelizer.

In a preferred embodiment, the system includes a frequency domainprogrammable channelizer as shown in FIG. 9 . The programmablechannelizer includes buffer services, pre-processing of the FFT binsamples, bin selection, at least one band pass filter (BPF), an inversefast Fourier transform (IFFT) function, decomposition, and/or frequencydown conversion and phase correction to yield baseband I,Q for channels1 through R. The IFFT function and decimation function are done toobtain each decomposed channel I,Q at the proper sampling rate.Advantageously, the frequency domain programmable channelizer is morecomputationally efficient than a time domain programmable channelizerbecause each filter is just a vector in the frequency domain and thefiltering operation is just a vector multiplication, decomposing theinput signal into multiple channels of differing bandwidths is parsingthe vector representing the input signal frequency domain content into asubvector of different length.

FIG. 10 is another embodiment of a programmable channelizer. Data entersthe filter and channel generators with channelization selector logic fora table lookup of filter coefficient and channelization vectors. Theprogrammable channelizer includes a comparison at each channel, whichprovides anomalous detection using a mask with frequency and power,which is then sent to the learning engine and/or the alarm system (“A”).Data processed with the FFT is sent to the blind detection engine and/orfor averaging processing (“B”). In one embodiment, average processingincludes blind detection of channel bandwidths and center frequency andcomparison to resulting frequency domain channelization. Data from thetable lookup of filter coefficient and channelization vectors undergoespreprocessing with a mix circular rotator to produce D₁ blocks of R₁points. A sum is taken of the D₁ block, and an R₁ point IFFT is taken toproduce discor overlap samples OL₁. This process occurs (e.g., inparallel) for D₁ blocks of R₁ points through D_(R) blocks of R_(R)points to produce OL₁ through OL_(R), which are then sent to theclassification engine (“C”). All data from the I,Q buffer is preferablystored in a buffered database (“D”). In one embodiment, the I,Q bufferis partitioned into N blocks with L oversamples. In one embodiment, theoriginal sample rate is decimated by D₁ where i is from 1 to R.

FIG. 53 illustrates one embodiment of a flow diagram of a channelizerconfiguration. Channel Definitions define the channels to detect.Channelization Vectors define the channels within the context of FFT.FFT Configuration configures FFT with sufficient resolution bandwidth(RBW) to resolve ambiguity among the Channel Definitions. A DeferenceMatrix identifies narrowband (NB) channels that are subsets of wideband(WB) channels to avoid false detection and/or to detect simultaneouschannels. Detector Configuration sets acceptable probability of FalseAlarm and probability thresholds for detection. Channel Detectiondetermines which channels are present subject to the DetectorConfiguration and the Deference Matrix.

In one embodiment, the FFT Configuration is operable to capture channelsin their entirety. In one embodiment, the FFT Configuration is operableto capture channels with a minimum number of points to support detectionprobabilities. In one embodiment, the FFT Configuration is operable toresolve ambiguities between channels by employing a sufficiently smallRBW.

In one embodiment, a channelization vector is operable to specifynormalized power levels per FFT bin for at least one channel (e.g., eachchannel). In one embodiment, the channelization vector power levels arepreferably normalized with respect to peak power in the channel'sspectrum envelope.

In one embodiment, a deference matrix is operable to identify channelswith bandwidth that falls within the bandwidth of other channels (i.e.,channels with wider bands). In one embodiment, the channels defer to thechannels with wider bands. In one embodiment, the detector is operableto evaluate narrower band channels before the detector evaluates widerband channels. In one embodiment, positive detection of wider bandchannels is operable to further constrain criteria that defines thepositive detection of deferring narrower band channels.

In one embodiment, noise floor estimation is operable to define the meanand standard deviation of noise from the average power FFT (e.g.,block). In one embodiment, statistics are operable to be on a channelspan level, a block containing multiple channels, or a bin level. In oneembodiment, the statistics are operable to be used by the channeldetector to set criteria for asserting channel detection.

In one embodiment, detector configuration is operable to set a minimumacceptable probability of false alarm. In one embodiment, the detectorconfiguration is operable to set a minimum acceptable probability ofmissed detection.

In one embodiment, channel detection is operable to evaluate presence ofchannels in order of increasing bandwidths (i.e., narrowband channelsbefore wideband channels). In one embodiment, the channel detection isoperable to perform a hypothesis test for each bin using Noise FloorEstimation and maximum probability of false alarm. In one embodiment,the hypothesis test includes a first hypothesis that a bin contains onlynoise (H₀) and/or a second hypothesis that a bin contains noise andsignal (H_(a)). In one embodiment, the channel detection is operable todetermine the probability that S≤s bins within a given channel'sbandwidth reject the “Noise only” hypothesis. In one embodiment, thechannel detection is operable to assert that a channel is detected ifthe probability is greater than p_(min). In one embodiment, the channeldetection is operable to adjust the hypothesis test for deferringnarrowband channels if detection probability is greater than p. In oneembodiment, the channel detection is operable to set a new mean and anew standard deviation.

FIG. 54 illustrates another embodiment of a flow diagram of achannelizer. The Detector Configuration is operable to set a probabilityof false alarm per bin (α_(bin)) and a minimum probability (prob_(min)).The noise modeler is operable to estimate a mean noise per bin (μ_(N))and a standard deviation per bin (σ_(N)). In one embodiment, the noisemodeler is operable to determine a channelization vector (CV_(bin)). Thechannelizer is operable to slice the FFT per channel and determinek_(obs) as the number of bins above the threshold in the channelizationvector. The probability calculator is operable to estimate β_(bin),determine the probability of missed detection (p_(md)), determine theprobability of false alarm (p_(fa) or p-value), and determine theprobability of detection being correct. The detector is operable todetermine that a signal is detected if the probability is greater orequal to the minimum probability. The detector is operable to determinethat a signal is not detected if the probability is less than theminimum probability.

FIG. 55 illustrates yet another embodiment of a flow diagram of achannelizer configuration. The noise modeler is operable to estimate amean noise per bin (μ_(N)) and a standard deviation per bin (σ_(N)). TheDetector Configuration is operable to set a probability of false alarmlevel per bin (α_(bin)), a power threshold (T_(pwr)), and a minimumprobability of being correct (prob_(min)). The channelizer is operableto slice the FFT per channel and determine k_(obs) as the number of binsabove the threshold in the channelization vector. The channelizer isoperable to determine a mean of the signal and noise per bin (μ_(N+S))and a standard deviation of the signal and noise per bin (σ_(N+S)). Theprobability calculator is operable to estimate β_(bin), determine theprobability of missed detection, determine the p-value, and determinethe probability of being correct. The detector is operable to determinethat a signal is detected if the probability is greater or equal to theminimum probability. The detector is operable to determine that a signalis not detected if the probability is less than the minimum probability.

FIG. 56A illustrates one embodiment of probability density functions perbin for noise and a signal with the noise. FIG. 56A includes a mean ofthe noise (μ_(H) ₀ ), a mean of the signal with the noise (μ_(H) _(a) ),a standard deviation of the noise (σ_(H) ₀ ), a standard deviation ofthe signal with the noise (σ_(H) _(a) ), a probability of falsedetection (α), a probability of missed detection (β), and acarrier-to-noise ratio (CNR). A minimum CNR is determined that satisfiesα and β given σ_(H) ₀ and σ_(H) _(a) . Additionally, given N bins ofchannel bandwidth (chbw), a number of bins that reject H₀ to determinepresence of a channel is determined.

In one embodiment, the probability of false detection (α) is calculatedusing the following equation:Pr{{circumflex over (μ)}=μ _(X) |H ₀∩(X≥CV)}=α

In one embodiment, the probability of correctly rejecting the noise onlyhypothesis is equal to the probability of getting as many as s bins withpower above the threshold if the noise only hypothesis is true. In oneembodiment, the probability of correctly rejecting the noise onlyhypothesis is calculated using the following equation:

${\Pr\left\{ {\left. {{ABT} \leq {abt}} \middle| {H_{0}\bigcap{N{Bins}}} \right. = {n{bins}}} \right\}} = {\begin{pmatrix}{n{bins}} \\{abt}\end{pmatrix}{\alpha^{abt}\left( {1 - \alpha} \right)}^{{nbins} - {abt}}}$where${abt} = {\sum\limits_{i = 1}^{N}\left( {X_{i} \geq {CV}} \right)}$

In one embodiment, the CNR is determined as follows:

${CNR} = \frac{\mu_{H_{a}}}{\mu_{H_{0}}}$

In another embodiment, the CNR is determined as follows:

${CNR} = \frac{\left( {\mu_{H_{a}} - {CV}} \right)}{\left( {{CV} - \mu_{H_{0}}} \right)}$

In one embodiment, CV=[μ_(H) ₀ +ϕ(1−α)σ_(H) ₀ ]. The above equation isoperable to be substituted as follows:

${CNR} = \frac{\left( {\mu_{H_{a}} - \left\lbrack {\mu_{H_{0}} + {{\phi\left( {1 - \alpha} \right)}\sigma_{H_{0}}}} \right\rbrack} \right)}{\left( {\left\lbrack {\mu_{H_{0}} + {{\phi\left( {1 - \alpha} \right)}\sigma_{H_{0}}}} \right\rbrack - \mu_{H_{0}}} \right)}$

The above equation is operable to be substituted as follows:

${CNR} = \frac{\left( {{CV} + {{\phi\left( {1 - \beta} \right)}\sigma_{H_{a}}} - \left\lbrack {\mu_{H_{0}} + {{\phi\left( {1 - \alpha} \right)}\sigma_{H_{0}}}} \right\rbrack} \right)}{\left( {\left\lbrack {\mu_{H_{0}} + {{\phi\left( {1 - \alpha} \right)}\sigma_{H_{0}}}} \right\rbrack - \mu_{H_{0}}} \right)}$

The above equation is operable to be substituted as follows:

${CNR} = \frac{\left( {\left\lbrack {\mu_{H_{0}} + {{\phi\left( {1 - \alpha} \right)}\sigma_{H_{0}}}} \right\rbrack + {{\phi\left( {1 - \beta} \right)}\sigma_{H_{a}}} - \left\lbrack {\mu_{H_{0}} + {{\phi\left( {1 - \alpha} \right)}\sigma_{H_{0}}}} \right\rbrack} \right)}{\left( {\left\lbrack {\mu_{H_{0}} + {{\phi\left( {1 - \alpha} \right)}\sigma_{H_{0}}}} \right\rbrack - \mu_{H_{0}}} \right)}$

The above equation is operable to be substituted as follows:

${CNR} = \frac{{\phi\left( {1 - \beta} \right)}\sigma_{H_{a}}}{{\phi\left( {1 - \alpha} \right)}\sigma_{H_{0}}}$

In yet another embodiment, the CNR is calculated as follows:

${CNR} = {\frac{\left( {s + n} \right)}{n} = {{\frac{s}{n} + 1} = {10^{{{SNR}\_{dB}}/10} + 1}}}$

The above equation is operable to be substituted as follows:CNR _(dB)=10 log₁₀(10^(SNR_dB/10)+1)

The above equation is operable to be substituted as follows:10^(CNR_dB/10)=10^(SNR_dB/10)+1

The above equation is operable to be substituted as follows:10^(CNR_dB/10)−1=10^(SNR_dB/10)

The above equation is operable to be substituted as follows:10 log₁₀(10^(CNR_dB/10)−1)[dB]=10 log₁₀(10^(SNR_dB/10))=SNR[dB]

The above equation is operable to be substituted as follows:10{circumflex over ( )}(10 log₁₀(10^(CNR_dB/10)−1))=SNR[mW]

FIG. 56B illustrates an example of a plurality of frequency bins and acritical value or threshold. In one embodiment, a frequency bin isassigned a value of “1” if it is above the critical value or thresholdand a value of “0” if it is below the critical value or threshold. Inthe example shown in FIG. 56B, nine frequency bins are assigned a valueof “1” and one frequency bin is assigned a value of “0.”

FIG. 56C illustrates another example of a plurality of frequency binsand a critical value or threshold. In the example shown in FIG. 56C,three frequency bins are assigned a value of “1” and seven frequencybins are assigned a value of “0.”

FIG. 57A illustrates one example of probability on the channel level. Inone embodiment, the system is operable to determine H₀, which is thehypothesis that the set of bins contains noise only. In one embodiment,the system is operable to determine H_(a), which is the hypothesis thatthe set of bins contains noise plus signal, making a possible channel.In one embodiment, the system is operable to calculate a threshold forthe probability of false alarm (k₀) on the channel level (α_(ch)). Inone embodiment, k₀ is calculated as follows:k ₀ =k|H ₀(α_(ch))

In one embodiment, the system is operable to calculate a threshold forthe probability of missed detection (k_(a)) on the channel level(β_(ch)). In one embodiment, k_(a) is equal to k_(obs). In oneembodiment, k_(a) is calculated as follows:k _(a) =k|H ₀(β_(ch))

In one embodiment, the system is operable to select a prob_(min),α_(ch), and β_(ch) (e.g., via manual input). In one embodiment, theprob_(min), the α_(ch), and the R_(ch) are set manually based on desiredgoals. For example, and not limitation, in one embodiment, theprobability of false alarm for a channel is set at <5% and theprobability of missed detection for a channel is set at <5%. In oneembodiment, the system is operable to determine k₀ given n bins, α_(ch),and α_(bn). In one embodiment, the system is operable to determineβ_(ch).

FIG. 57B illustrates one example of probability on the bin level. In oneembodiment, a power threshold for the probability of false alarm on thebin level is calculated. In one embodiment, the channelization vector iscalculated using the following equation:CV=dBm/H ₀(α_(bin))

FIG. 58A illustrates an example showing a critical value (γ), powerlevels of a noise signal, and power levels of a signal with noise. Inthe example shown in FIG. 58A, the received noise mean and variance isestimated. The noise power is assumed to be Gaussian and independent andidentically distributed. The probability of false alarm at the bin level(α) is specified. The critical value is set accordingly. The receivedmean and variance is measured, and the signal is placed into N adjacentfrequency bins. The signal with noise power is also assumed to beGaussian and independent and identically distributed. The probability ofmissed detection at the bin level (β) is determined with respect to thecritical value.

FIG. 58B illustrates an example of the probability of false alarm for anoise signal.

FIG. 58C illustrates an example of the probability of missed detectionfor a signal with noise.

FIG. 59 illustrates one example of probabilities. In the example shownin FIG. 59 , the probability of false detection (α) is equal to 0.05 andthe probability of missed detection (β) is equal to 0.05. Theprobability of channel presence is estimated as follows when theprobability of false detection and the probability of missed detectionare small:probability of channel presence=[1−(α+β)]Thus, for α=0.05 and β=0.05, the probability of channel presence=0.9.

FIG. 60 illustrates an example of equations for hypothesis testing withchannels for the following scenarios: (1) Noise Only (No Signal), AssertNoise Only, (2) Noise Only (No Signal), Assert Noise and Signal, (3)Noise and Signal, Assert Noise Only, and (4) Noise and Signal, AssertNoise and Signal. Assert Noise Only is used when n-k bins or more belowthe threshold. Assert Noise and Signal is used when k bins or more areabove the threshold.

In one embodiment, Noise Only (No Signal), Assert Noise Only uses thefollowing equation:

${\Pr\left\{ \text{“N”} \middle| N \right\}} = {1 - {\sum\limits_{u = k}^{n}{\begin{pmatrix}n \\u\end{pmatrix}{\alpha^{u}\left( {1 - \alpha} \right)}^{n - u}}}}$

In one embodiment, Noise Only (No Signal), Assert Noise and Signal usesthe following equation:

${\Pr\left\{ {\text{“N”}\text{“+”}\text{“S”}} \middle| N \right\}} = {1 - {\sum\limits_{u = k}^{n}{\begin{pmatrix}n \\u\end{pmatrix}{\alpha^{u}\left( {1 - \alpha} \right)}^{n - u}}}}$

In one embodiment, Noise and Signal, Assert Noise Only uses thefollowing equation:

${\Pr\left\{ \text{“N”} \middle| {N + S} \right\}} = {1 - {\sum\limits_{c = {n - k}}^{n}{\begin{pmatrix}n \\c\end{pmatrix}{\beta^{c}\left( {1 - \beta} \right)}^{n - c}}}}$

In one embodiment, Noise and Signal, Assert Noise and Signal uses thefollowing equation:

${\Pr\left\{ {\text{“N”}\text{“+”}\text{“S”}} \middle| {N + S} \right\}} = {1 - {\sum\limits_{c = {n - k}}^{n}{\begin{pmatrix}n \\c\end{pmatrix}{\beta^{c}\left( {1 - \beta} \right)}^{n - c}}}}$

FIG. 61 illustrates one example of an algorithm used in a channelizer.The algorithm 6100 includes estimating the bin-wise noise model 6102. Inone embodiment, the bin-wise noise model is obtained from a noise floorestimator. In one embodiment, the bin-wise noise model is calculatedusing the following equation:H ₀=

(μ_(N),σ_(N) ²)

The algorithm 6100 includes estimating the bin-wise noise model 6104. Inone embodiment, the bin-wise noise and signal model is obtained from theFFT or block FFT. In one embodiment, the bin-wise noise and signal modelis calculated using the following equation:H _(a)=

(μ_(NS),σ_(NS) ²)

The algorithm 6100 includes determining a bin-level probability of falsealarm (P_(FA)=α_(bin)) 6106. The algorithm 6100 further includesdetermining a bin-level threshold 6108. In one embodiment, the bin-levelthreshold is calculated using the following equation:τ_(bin) =Q ⁻¹(α_(bin) |H ₀)where Q(⋅) is the complementary error function (ERFC) for Gaussiandistributions.

The algorithm 6100 includes determining a channel-level probability offalse alarm (α_(chan)) 6110. The algorithm 6100 further includesdetermining a channel-level threshold 6112. In one embodiment, thechannel-level threshold is calculated using the following equation:τ_(chan) =Q ⁻¹(α_(chan) |[H ₀ ,n,α _(bin)])

The algorithm 6100 includes calculating a detection vector (v) 6114. Inone embodiment, an element of the detection vector is calculated asfollows:

$= \left\{ \begin{matrix}{0,} & {{{if}x_{i}} < \tau_{bin}} \\{1,} & {{{if}x_{i}} \geq \tau_{bins}}\end{matrix} \right.$

The algorithm 6100 includes counting the number of detection vectorelements equal to 1 and comparing to k 6116. The algorithm 6100 furtherincludes determining the p-value 6118, where the p-value is theprobability of false alarm. In one embodiment, the p-value is calculatedusing the following equation:

${p - {value}} = {1 - {\sum_{i = 0}^{k}{\begin{pmatrix}n \\i\end{pmatrix}{\alpha_{bin}^{i}\left( {1 - \alpha_{bin}} \right)}^{n - i}}} + {\begin{pmatrix}n \\k\end{pmatrix}{\alpha_{bin}^{k}\left( {1 - \alpha_{bin}} \right)}^{n - k}}}$

The algorithm 6100 includes determining the probability of misseddetection (β_(chan)) 6120. In one embodiment, the probability of misseddetection is calculated using the following equation:

$P_{MD} = {\beta_{chan} = {{\phi\left( {H_{a},n,\tau_{chan},\alpha_{bin}} \right)} = {\sum\limits_{i = 0}^{\tau_{chan}}{\begin{pmatrix}n \\i\end{pmatrix}{\alpha^{i}\left( {1 - \alpha} \right)}^{n - i}}}}}$

The algorithm 6100 includes determining the overall detectionprobability 6122. In one embodiment, the overall detection probabilityis calculated using the following equation:probability=(1−P _(FA))(1−P _(MD))

FIGS. 62A-62G illustrate examples of information provided in at leastone graphical user interface (GUI). FIG. 62A illustrates one example ofa simulated FFT, noise floor estimation, and comparison withchannelization vectors. In the example shown in FIG. 62A, FFT Frame #1has a threshold of −59.7 dBm and a noise floor estimate of −99.7 dBm.

FIG. 62B illustrates channelization vectors and comparisons for the FFTframe shown in FIG. 62A. A smaller channel bandwidth leads to a higherprobability of detection than a larger channel bandwidth. The smallerchannel bandwidth has a probability of 100% in the lower frequency binsand a probability of 53% in the higher frequency bins for the signalabove the threshold. The larger channel bandwidth has a probability of34% in the lower frequency bins.

FIG. 62C illustrates amplitude probability distribution for the FFTframe shown in FIG. 62A.

FIG. 62D illustrates FFT frames grouped by block for the FFT frame shownin FIG. 62A.

FIG. 62E illustrates average power FFT by block for the FFT frame shownin FIG. 62A.

FIG. 62F illustrates the channelization vectors for the FFT frame shownin FIG. 62A. The x-axis indicates the frequency of the bin.

FIG. 62G illustrates the comparison vectors for the FFT frame shown inFIG. 62A.

FIG. 63A illustrates one embodiment of preemption by wideband detection.In one embodiment, the system determines which narrowband channels areproper subsets of wideband channels. In one embodiment, this isrecursive across multiple layers. In one embodiment, the FFT is comparedto wideband channels after narrowband channels. In one embodiment, ifthe minimum threshold for detection is satisfied for a channel, thatchannel is considered detected. In one embodiment, detection ofnarrowband channels that are proper subsets of wideband channels ispreempted. In the example shown in FIG. 63A, channels D, E, and B aredetected, and channels A, F, C, and G are not detected. Detection ofchannel F is preempted by detection of B.

FIG. 63B illustrates the example shown in FIG. 63A accounting forwideband detection. In one embodiment, the system determines whichnarrowband channels are proper subsets of wideband channels. In oneembodiment, this is recursive across multiple layers. In one embodiment,the FFT is compared to wideband channels after narrowband channels. Inone embodiment, if the minimum threshold for detection is satisfied fora channel, that channel is considered detected. In the embodiment shownin FIG. 63B, the average power of the wideband channel is subtractedfrom the power observed in the FFT slice within the narrowband channel.Alternatively, the bin-level threshold is adjusted. In one embodiment,if the minimum threshold for detection is satisfied for the adjustednarrowband channel, it is considered detected. In the example shown inFIG. 63B, channels D, E, B, and F are detected, and channels A, C, and Gare not detected.

FIG. 64 illustrates one embodiment of maximum resolution bandwidth (RBW)for vector resolution. The maximum RBW is greatest common factor of allspacings (δ) from all channels and of the span. A deference matrix isoperable to be created showing which overlapping channels, if bothindividually detected, take precedence in an either-or detection scheme.In one embodiment, it is also possible to detect both channels withvarying levels of likelihood.

FIG. 65A illustrates one example of a spectrum scenario. The setincludes 0 and/or 1. That is, S={0,1}. The number of permutations isequal to 2^(N), where N is the number of bins. For example, if thenumber of bins is 50, the number of permutations is equal to 2⁵⁰. If thenumber of bins is 100, the number of permutations is equal to 2¹⁰⁰.

FIG. 65B illustrates an embodiment of channelization vectors. In oneembodiment, the channelization vector includes a smaller bandwidth(bw1), resulting in two channels (channels 1.1 and channel 1.2). Inanother embodiment, the channelization vector includes one channel(channel 2.1).

FIG. 66A illustrates one example of bandwidth selection. In the exampleshown in FIG. 66A, bandwidth 1 (bw1) is 100 bins, bandwidth 2 (bw2) is200 bins, and bandwidth 3 (bw3) is 600 bins.

FIG. 66B illustrates an example using the bandwidth selection in FIG.66A. In the example shown in FIG. 66B, the first two channels detect asignal in bw1, the first channel detects a signal in bw2, and thechannel only detects a signal in ⅓ of bw3. Therefore, no signal isdetected in bw3.

FIGS. 67A-67D illustrate additional examples of information provided inat least one graphical user interface (GUI). FIG. 67A illustratesanother example of an FFT frame illustrating the center frequency on thex-axis and the frequency bin mean power level (Fbpower_mean) on they-axis. The example shown in FIG. 67A includes a plurality of thresholdpowers (e.g., Tpwr3, Tpwr8, Tpwr13).

FIG. 67B illustrates channelization vectors and comparisons for the FFTframe shown in FIG. 67A. The graph illustrates possible channel flowcomposition (chflo) and their probabilities. As shown in FIG. 67B, Tpwr3and Tpwr8 are detected, while Twpr13 is not detected.

FIG. 67C illustrates a graph for the FFT frame shown in FIG. 67A. Thegraph illustrates the number of bins of a particular power level (massfunction) per channel. The x-axis is the percentage of bins at aparticular power level for the channel. For example, around 15% of thebins in the channel are at a level of −95 dbfs.

FIG. 67D illustrates a table for the FFT frame shown in FIG. 67A. Thetable provides a channel index, a count, and a number of bins above thethreshold for that channel. The table shows for different channelindexes a minimum number of bins of signal with noise to determine thatthe channel is occupied with a probability of false alarm below thedesired threshold.

FIGS. 68A-68C illustrate further examples of information provided in atleast one graphical user interface (GUI). FIG. 68A illustrates anexample of spectrum by channel.

FIG. 68B illustrates an example of channel detection for the spectrumshown in FIG. 68A.

FIG. 68C illustrates an example of a cascade for the spectrum shown inFIG. 68A.

FIG. 69A illustrates an example of a graph of total signal power. In oneembodiment, total signal power is calculated using the followingequation:

${{Total}{Signal}{Power}} = {\frac{1}{chbw}{\sum}_{b}P_{s,i}}$

FIG. 69B illustrates an example of a graph of total noise power. In oneembodiment, total noise power is calculated using the followingequation:

${{Total}{Noise}{Power}} = {\frac{1}{chbw}{\sum}_{b}P_{n,i}}$

In one embodiment, the signal to noise ratio is calculated using thefollowing equation:

${SNR} = \frac{{\sum}_{b}P_{s,i}\delta f}{{\sum}_{b}P_{n,i}\delta f}$

FIGS. 70A-70C illustrate further examples of information provided in atleast one graphical user interface (GUI). FIG. 70A illustrates anotherexample of a simulated FFT, noise floor estimation, and comparison withchannelization vectors. In the example shown in FIG. 70A, FFT Frame #6has a threshold of −59.2 dBm and a noise floor estimate of −99.2 dBm.

FIG. 70B illustrates channelization vectors and comparisons for the FFTframe shown in FIG. 70A. As described previously, a smaller channelbandwidth leads to a higher accuracy of detection than a larger channelbandwidth. The smaller channel bandwidth has a probability of 100% and95% in the two lowest frequency bins and a probability of 74% in thehighest frequency bin for the signal above the threshold. The largerchannel bandwidth has a probability of 74% in the lowest frequency binand 98% in the second frequency bin. However, the larger channelbandwidth does not account for the drop in signal shown in the thirdfrequency bin of the smaller channel bandwidth, which results in only a40% probability of detection in the smaller channel bandwidth.

FIG. 70C illustrates channelization vectors and comparisons for the FFTframe shown in FIG. 70A.

FIG. 11 illustrates one embodiment of a blind detection engine. In oneembodiment, the blind detection engine is operable to estimate a numberof channels and their bandwidths and center frequencies using only anaveraged power spectral density (PSD) of the captured signal. Data fromthe programmable channelizer undergoes an N point FFT. A power spectraldensity (PSD) is calculated for each N point FFT and then a complexaverage FFT is obtained for the P blocks of N point FFT. The PSD is sentto a noise floor estimator, an edge detection algorithm, and/or anisolator. Noise floor estimates from the noise floor estimator are sentto the signal database. The edge detection algorithm passes informationto a signal separator (e.g., bandwidth, center frequency). The isolatorobtains information including, but not limited to, PSD, the bandwidthand center frequency per channel, the complex average FFT, and/or the Npoint FFT. Information from the isolator is sent to the programmablechannelizer, the envelope feature extraction module, and/or theclassification engine.

FIG. 12 illustrates one embodiment of an edge detection algorithm. Peaksare detected for all power values above the noise floor. Peaks arerecorded in a power array and/or an index array. Consecutive powervalues are found by looping through the arrays. For each group ofconsecutive power values, a sub-power array and/or a sub-index array arecreated. The blind detection engine steps through each power valuestarting with a default rising threshold. If N consecutive values areincreasing above the rising threshold, a first value of N values is setas the rising edge and the index of the first value of N values isrecorded. The Nth value is recorded as a rising reference point. Therising threshold is updated based on the rising reference point, and theblind detection engine continues to scan for rising values. If the blinddetection engine does not detect rising values and detects M consecutivevalues decreasing below a falling threshold, a first value of M valuesis set as the falling edge and the index of the first value of M valuesis recorded. The Mth value is recorded as a falling reference point. Thefalling threshold is updated based on the falling reference point. Inone embodiment, x is a value between 1 dB and 2.5 dB. In one embodiment,y is a value between 1 dB and 2.5 dB.

The blind classification engine receives information from the blinddetection engine as shown in FIG. 13 . Signals are separated based onbandwidth and/or other envelope properties (e.g., duty cycle). An IFFTis performed on R signals for narrowband and/or broadband signals.Decimation is then performed based on bandwidth. Moment calculations areperformed for each signal I,Q using the decimated values and/orinformation from the channelizer. In a preferred embodiment, the momentcalculations include a second moment and/or a fourth moment for eachsignal. A match based on cumulants is selected for each I,Q stream,which is sent to the demodulation bank and/or the geolocation engine.

From the definitions of the second and fourth moments, the followingequations are used to calculate the cumulants:

${\hat{C}}_{20} = {\frac{1}{N}{\sum\limits_{n = 1}^{n = N}{❘{Y(n)}❘}^{2}}}$${\overset{\hat{}}{C}}_{21} = {\frac{1}{N}{\sum\limits_{n = 1}^{n = N}{Y^{2}(n)}}}$${\overset{\hat{}}{C}}_{40} = {{\frac{1}{N}{\sum\limits_{n = 1}^{n = N}{Y^{4}(n)}}} - {3{\overset{\hat{}}{C}}_{20}^{2}}}$${\overset{\hat{}}{C}}_{41} = {{\frac{1}{N}{\sum\limits_{n = 1}^{n = N}{{Y^{3}(n)}{Y^{*}(n)}}}} - {3{\overset{\hat{}}{C}}_{20}{\overset{\hat{}}{C}}_{21}}}$${\overset{\hat{}}{C}}_{42} = {{\frac{1}{N}{\sum\limits_{n = 1}^{n = N}{❘{Y(n)}❘}^{4}}} - {❘{\overset{\hat{}}{C}}_{20}❘}^{2} - {2{\overset{\hat{}}{C}}_{21}^{2}}}$

If it assumed that transmitted constellations are normalized to unityaverage power, which is easily completed by a power factor equal to 0dB, this results in Ĉ₂₁≈1. To calculate a normalized fourth moment iscalculated using the following equation:

${{\overset{\sim}{\hat{C}}}_{4J}\overset{\bigtriangleup}{=}{{{\overset{\hat{}}{C}}_{4J}/{\overset{\hat{}}{C}}_{21}{for}J} = 0}},1,2$

Advantageously, normalizing the fourth moment cumulants removes anyscaling power problems.

FIG. 14 illustrates details on selection match based on cumulants formodulation selection. As previously described, the cumulants preferablyinclude a second moment and/or a fourth moment for each signal. Forexample, a fourth moment between −0.9 and 0.62 is a quadrature amplitudemodulation (QAM) signal, a fourth moment greater than or equal to 1 isan amplitude modulation (AM) signal, a fourth moment equal to −1 is aconstant envelope signal (e.g., frequency modulation (FM), Gaussianminimum-shift keying (GMSK), frequency-shift keying (FSK), orphase-shift keying (PSK)), a fourth moment between −1.36 and 1.209 is apulse-amplitude modulation (PAM) signal, and a fourth moment equal to −2is a binary phase-shift keying (BPSK) signal. A type is selected using alook up table, the signal I,Q is labeled with the type, and theinformation is sent to the demodulation bank.

Additional information about selection match based on cumulants formodulation selection is available in Table 1 below.

TABLE 1 Type {tilde over (Ĉ)}₄₀ {tilde over (Ĉ)}₄₂ σ({tilde over (Ĉ)}₄₀)σ({tilde over (Ĉ)}₄₂) AM >1.0 FM −1 GMSK −1 FSK −1 BPSK −2.00 −2.00 0 0PAM (4) −1.36 −1.36 2.56 2.56 PAM (8) −1.238 −1.238 4.82 4.82 PAM (16)−1.2094 −1.2094 5.52 5.52 PSK (4) −1.00 −1.00 QAM (4) −0.68 −0.68 QAM(16) −0.64 −0.64 3.83 2.24 QAM (32) −0.61 −0.61 3.89 2.31

FIG. 15 illustrates a flow diagram according to one embodiment of thepresent invention. Data in the I/Q buffer is processed using a libraryof functions. The library of functions includes, but is not limited to,FFT, peak detection, characterization, and/or rate adjustment. Aspreviously described, the system preferably includes at least one dataanalysis engine. In one embodiment, the at least one data analysisengine includes a plurality of engines. In one embodiment, the pluralityof engines includes, but is not limited to, a detection engine, aclassification engine, an identification engine, a geolocation engine,and/or a learning engine. Each of the plurality of engines is operableto interact with the other engines in the plurality of engines. Thesystem is operable to scan for occupancy of the spectrum, create a mask,detect drones, and/or analyze data.

The control panel manages all data flow between the I/Q buffer, libraryfunctions, the plurality of engines, applications, and user interface. Acollection of basic functions and a particular sequence of operationsare called from each of the plurality of engines. Each of the pluralityof engines is operable to pass partially processed and/or analyzed datato other engines to enhance functionality of other engines and/orapplications. The data from the engines are then combined and processedto build applications and/or features that are customer or marketspecific.

In one embodiment, a plurality of state machines performs a particularanalysis for a customer application. In one embodiment, the plurality ofstate machines is a plurality of nested state machines. In anotherembodiment, one state machine is utilized per each engine application.The plurality of state machines is used to control flow of functionsand/or an engine's input/output utilization to perform requiredanalyses.

FIG. 16 illustrates control panel functions according to one embodiment.The control panel is operable to detect occupation of the spectrum,activate an alarm, perform drone detection and direction finding,geolocation, artificial spectrum verification, and provide at least oneuser interface. The at least one user interface is preferably agraphical user interface (GUI). The at least one user interface (UI) isoperable to display output data from the plurality of engines and/orapplications. In one embodiment, the at least one UI incorporates thirdparty GIS for coordinate display information. The at least one UI isalso operable to display alarms, reports, utilization statistics, and/orcustomer application statistics. In one embodiment, the at least one UIincludes an administrator UI and at least one customer UI. The at leastone customer UI is specific to each customer.

In one embodiment, the systems and methods of the present inventionprovide unmanned vehicle (e.g., drone) detection. The overall system iscapable of surveying the spectrum from 20 MHz to at least 6 GHz, notjust the common 2.4 GHz and 5.8 GHz bands as in the prior art. Thesystems and methods of the present invention are operable to detect UVsand their controllers by protocol.

In one embodiment, the systems and methods of the present inventionmaintain a state-of-the-art learning system and a protocol library forclassifying detected signals by manufacturer and controller type. Thestate-of-the-art learning system and the protocol library are updated asnew protocols emerge.

In one embodiment, classification by protocol chipset is utilized toprovide valuable intelligence and knowledge for risk mitigation andthreat defense. The valuable intelligence and knowledge includeeffective operational range, supported peripherals (e.g., external orinternal camera, barometers, global positioning system (GPS) and deadreckoning capabilities), integrated obstacle avoidance systems, andinterference mitigation techniques.

Advantageously, the system is operable to detect drones that are not inthe protocol library. Further, the system is operable to detect droneswithout demodulating command and control protocols. In one embodiment,the system does not include a protocol library. New protocols and newdrones are constantly being released. Additionally, a nefarious operatorcan switch out the chipset of a drone, which would leave an areavulnerable to the modified drone because a system would not be able toidentify the signal as a drone if the protocol is not in the protocollibrary. In one embodiment, the system generates actionable data thatindicates that at least one signal is behaving like a drone. The systemperforms blind detection, which allows the system to detect the dronesignal without the protocol library. In one embodiment, the system isoperable to detect drones by evaluating an envelope of the command andcontrol signal. In one embodiment, the system detects the drone signalbased on a duty cycle and/or changes in power levels of the signalenvelope. In one example, an LTE signal is classified by the system as adrone when moving at a high velocity.

FIG. 17 illustrates one embodiment of an RF analysis sub-architecture ofthe system. The control panel interacts with the I/Q buffer, libraryfunctions, engines, applications, and/or user interface. The enginesinclude a data analysis engine. Analyzed data from the data analysisengine results in an alarm when an alarm condition is met. The alarm istransmitted via text and/or email, or is visualized on a graphical userinterface (GUI) of at least one remote device (e.g., smartphone, tablet,laptop computer, desktop computer).

FIG. 18 illustrates one embodiment of a detection engine of the system.The detection engine receives data from the at least one monitoringunit. the detection engine includes blind feature extraction algorithms.A mask is created. The detection engine then performs a mask utilizationrating and the mask is compared to previous masks. Anomalies are thendetected.

As previously described, in one embodiment, the data analysis engine isoperable to perform mask creation and analyze an electromagnetic (e.g.,RF) environment using masks. Mask creation is a process of elaborating arepresentation of an electromagnetic environment by analyzing a spectrumof signals over a certain period of time. A mask is created with adesired frequency range (e.g., as entered into the system via userinput), and FFT streaming data is also used in the mask creationprocess. A first derivative is calculated and used for identifyingmaximum power values. A moving average value is created as FFT data isreceived during a selected time period for mask creation (e.g., via userinput). For example, the time period is 10 seconds. The result is an FFTarray with an average of maximum power values, which is called a mask.FIG. 19 illustrates a mask according to one embodiment of the presentinvention.

In one embodiment, the mask is used for electromagnetic environmentanalysis. In one embodiment, the mask is used for identifying potentialunwanted signals in an electromagnetic (e.g., RF) environment. Thesystem is operable to utilize masks based on a priori knowledge and/ormasks based on expected behavior of the electromagnetic environment.

Each mask has an analysis time. During its analysis time, a mask isscanned and live FFT streaming data is compared against the mask beforenext mask arrives. If a value is detected over the mask range, a triggeranalysis is performed. Each mask has a set of trigger conditions, and analarm is triggered into the system if the trigger conditions are met. Inone embodiment, there are three main trigger conditions including analarm duration, a decibel (dB) offset, and a count. The alarm durationis a time window an alarm needs to appear to be considered a triggercondition. For example, the time window is 2 seconds. If a signal isseen for 2 seconds, it passes to the next condition. The dB offset is athreshold value (i.e., dB value) a signal needs to be above the mask tobe considered as a potential alarm. The count is the number of times thefirst two conditions need to happen before an alarm is triggered intothe system.

FIG. 20 illustrates a workflow of automatic signal detection accordingto one embodiment of the present invention. A mask definition isspecified by a user for an automatic signal detection process includingcreating masks, saving masks, and performing electromagnetic (e.g., RF)environment analysis based on the masks created and FFT data stream froma radio server. In one embodiment, if trigger conditions are met, alarmsare triggered and stored to a local database for visualization.

FIG. 21 illustrates components of a Dynamic Spectrum Utilization andSharing model according to one embodiment of the present invention. Byemploying the Dynamic Spectrum Utilization and Sharing model, thepresent invention is operable to perform a plurality of radio frequency(RF) environmental awareness functionalities including, but not limitedto, monitoring and/or detection, identification, and/or classification.Monitoring and/or detection functionalities include, but are not limitedto, broadband frequency range detection, wideband capture in real-timeor near-real-time, initial processing and/or post event processing,24-hour autonomous monitoring, and/or reconfiguration options relatingto time, frequency, and spatial settings. Identification functionalitiesinclude, but are not limited to, anomalous signal detection, anomaloussignal flagging, anomalous signal time stamp recording, providing ananomalous signal database, and/or utilization of a spectrum mask. In oneembodiment, the spectrum mask is a dynamic spectrum mask. Classificationfunctionalities include, but are not limited to, correlating signalevents with known signal protocols, correlating signal events with knownvariables, correlating signal events with known databases, correlatingsignal events with existing wireless signal formats, and/or correlatingsignal events with existing cellular protocol formats. Each of theaforementioned functionalities incorporates learning processes and/orprocedures. These include, but are not limited to, historical dataanalysis, data preservation tools, and/or learning analytics.Incorporation of machine learning (ML), artificial intelligence (AI),and/or neural networks (NN) ensures that every aspect of detection,monitoring, identification, and/or classification is performedautonomously. This is compounded through the use of the learninganalytics, enabling the use of utilization masks for continual ML,prediction modeling, location analysis, intermodulation analysis, and/orthe integration of third-party data sets for increasing overall learningcapabilities and/or functionalities of the platform. Moreover, thesecapabilities and/or functionalities are backed up through secure datapreservation services, providing both a secure platform environmentand/or data enforcement documentation (i.e., legal documents).Furthermore, the platform is operable to provide automatednotifications, programmable event triggers, customizable rules and/orpolicies, and Tip and Cue practices. Automated notifications include,but are not limited to, alerts, alarms, and/or reports. Advantageously,this functionality enables the platform to react to specific rulesand/or policies, as well as incorporating the platform's own awarenessand knowledge, creating an optimized platform for any RF environmentand/or mission.

Prediction models used by the platform provide an accurate insight intothe dynamic spectrum allocation and utilization functionalities. Theseprediction models enable the platform to autonomously create forecastsfor future spectrum usage. In addition, the prediction models used bythe platform incorporate descriptive analytics, diagnostic analytics,predictive analytics, and/or prescriptive analytics. Descriptiveanalytics refers specifically to the data stored, analyzed, and/or usedby the platform. Descriptive analytics provides data enabling theplatform to act and/or provide a suggested action. Diagnostic analyticsrefers to how and/or why the descriptive analytics acted and/orsuggested an action. Predictive analytics specifically refers to theutilization of techniques including, but not limited to, ML, AI, NNs,historical data, and/or data mining to make future predictions and/ormodels. Prescriptive analytics refers to the act and/or the suggestedact generated by the descriptive analytics. Once this predictive modelis in place, the platform is operable to recommend and/or performactions based on historical data, external data sources, ML, AI, NNs,and/or other learning techniques.

FIG. 22 illustrates a Results model according to one embodiment of thepresent invention. The Results model provided by the present inventionis centered around four core practices: proactive, predictive,preventative, and preservation. The predictive practice refers to usingthe aforementioned learning functionalities and capabilities to evolvethe platform, enabling the characterization of events that led up to aninterference scenario and/or performing interference source modeling toforecast future probabilities and/or conflicting events. The predictivepractice is intertwined with the platform remaining proactive,identifying possible signals of interference. While identifying possiblesignals of interference is a combination of the platform's predictiveand proactive capabilities, the platform also remains proactive inperforming wireless location characterization for both pre- andpost-event scenarios. In addition, the platform's proactive capabilitiesinclude, but are not limited to, identifying all possible sources ofconflict based on prior events. Furthermore, the platform also focuseson preventative practices. These include, but are not limited to,maintaining a set of de-confliction rules, providing trigger warningnotifications and/or early warning notifications, and/or maintainingcompatibility with multiple government agencies, including correspondinggovernment project management offices (PMOs) and any interferingsources. In one embodiment, the platform automatically establishes theset of de-confliction rules, where the set of de-confliction rules areoperable for editing. In one embodiment, the platform is operable toautonomously edit the set of de-confliction rules. In anotherembodiment, the platform enables editing of the set of de-conflictionrules via user input. Finally, the platform includes preservationcomponents and/or functionalities. These include, but are not limitedto, evidentiary storage, learning capabilities, and modelingfunctionality. Each of these four core practices is interconnectedwithin the platform, enabling dynamic spectrum utilization and sharing.

Geolocation

Geolocation is an additional aspect relating to electromagnetic (e.g.,RF) analysis of an environment. The primary functions of theelectromagnetic analysis of the environment include, but are not limitedto, detection, classification, identification, learning, and/orgeolocation. Additionally, the electromagnetic analysis is operable tooutput environmental awareness data.

The system includes a geolocation engine, operable to use both passiveand/or active methods of radio geolocation. In general, radiogeolocation refers to the geographic location of man-made emittersources propagating using radio (electromagnetic) waves as they impingeupon a man-made geo-locator, or receiver. Passive radio geolocationrequires no transmission of signals by a geo-locator, whereas activeradio geolocation involves a geolocator transmitting signals thatinteract with an emitter source. Passive methods of geolocation include,but are not limited to, single directional beam antenna response,multidirectional beam antenna response (Amplitude Ratio), multi-antennaelement response (Array Processing), line of bearing (LOB)-to-positionsolutions, and/or general optimization. Multi-antenna element responsemethods include, but are not limited to, phase interferometry,beamforming, conventional array manifold processing approaches, and/orhigh-resolution array manifold processing approaches using signalssubspace. While these passive methods primarily apply to approaches forDirection Finding (DF) as spatial filtering, passive methods that applyto approaches other than DF as spatial filtering are operable for use bythe system. DF refers to the process of estimating the direction ofarrival of propagating emitter signals as they impinge on a receiver.Passive methods further include DF approaches based on generaloptimization including, but not limited to, digital pre-distortion(DPD), convex programming, and/or distributed swarm approaches.

In addition to the previously mentioned passive approaches, the systemis operable to apply approaches based on ranging observations including,but not limited to, receiver signal strength indicators (RSSI), time ofarrival (TOA), and/or time difference of arrival (TDOA) methods. RSSIapproaches relate to the generation of observable data and/or locationestimation. TOA and/or TDOA approaches relate to generating observabledata from distributed multi antenna systems and/or single antennasystems, and/or location estimation using non-linear optimization and/orconstraint linear optimization.

In a preferred embodiment, geolocation is performed using Angle ofArrival (AOA), Time Difference of Arrival (TDOA), Frequency Differenceof Arrival (FDOA), and power distribution ratio measurements.

FIG. 23 is a table listing problems that are operable to be solved usingthe present invention, including serviceability, interference,monitoring and prediction, anomalous detection, planning, compliance,and/or spectrum sharing or leasing.

FIG. 24 illustrates a passive geolocation radio engine system viewaccording to one embodiment of the present invention. First, a radiofrequency (RF) front end receives at least one RF signal. The RF frontend includes, but is not limited to, a set of sensors, a sensorsubsystem, at least one analog to digital converter (ADC), and/or an ADCsensor processing subsystem. Once the at least one RF signal has beenanalyzed by the RF front end and/or the sensor subsystem, the at leastone RF signal becomes at least one analyzed RF signal. The at least oneanalyzed RF signal is output to a measurement subsystem. The measurementsubsystem is operable to generate radio location measurements. The radiolocation measurements are envelope-based and/or signalcharacteristic-based. The measurement subsystem is further operable togenerate contextual measurements and/or conventional measurementsrelating to TOA, AOA, TDOA, receiver signal strength (RSS), RSSI, and/orFDOA. The generated conventional measurements are then analyzed usingposition algorithms, further enhancing measurement accuracy. Once thecontextual measurements are generated and/or the conventionalmeasurements are analyzed using position algorithms, the at least oneanalyzed RF signal is sent to a position engine subsystem. The positionengine subsystem includes a position display. Each of the previouslymentioned components, systems, and/or subsystems are operable fornetwork communication.

The geolocation engine is operable to use a plurality of algorithms todetermine a location of the at least one signal. The plurality ofalgorithms includes, but is not limited to, TDOA, FDOA, AOA, power levelmeasurements, and/or graphical geolocation, which is described below.The geolocation is operable to autonomously decide what algorithm(s) touse to determine the location.

FIG. 25 illustrates one embodiment of a method to autonomously selectone or more of the plurality of algorithms. Timing and carrier frequencyoffset corrections are performed on I,Q data and sent to the signaldetection engine. The I,Q data (e.g., I,Q₀, I,Q₁, I,Q₂, I,Q₃) is sent tothe signal detection engine. Information from the signal detectionengine is sent to the blind classification engine. Information from theblind classification engine is sent to the demodulation bank. Errorestimates are performed on envelope (Doppler) measurements from thesignal detection engine, signal (time) domain measurements from theblind classification engine, and timing, protocol, and Dopplermeasurements from the demodulation bank. An evaluation of fidelity isapproximately equal to an SNR of the envelope measurements (λ₁), signalmeasurements (λ₂), and protocol measurements (λ₃). Error analysis forAOA, TDOA, correlation ambiguity function (CAF) for graphicalgeolocation, FDOA, and power ratio are used in the evaluation offidelity. C_(t) is calculated and minimized over all methods to selectthe at least one geolocation method, where C_(t) is the cost function tobe minimized and t denotes a time block used to calculate thegeolocation solution.

In one embodiment, the geolocation engine uses graphical geolocationtechniques. An area is pictorially represented in a grid. Resolution ofthe grid determines a position in space. The system is operable todetect the at least one signal in the space and determine a location ofthe at least one signal using the graphical geolocation techniques. Inone embodiment, outputs (e.g., location) to a non-linear equation areused to determine possible inputs (e.g., power measurements). Thepossible outputs are placed on a two-dimensional map. Inputs are thenmapped to form a hypothesis of possible outputs. In one embodiment, thegraphical geolocation techniques include an image comparison between thetwo-dimensional map of the possible outputs and the signal data. Inanother embodiment, the graphical geolocation techniques further includetopology (e.g., mountains, valleys, buildings, etc.) to create athree-dimensional map of the possible outputs. The graphical geolocationtechniques in this embodiment include an image comparison between thethree-dimensional map of the possible outputs and the signal data.

The geolocation engine is operable to make use of spinning DF, throughthe use of rotating directional antennas and estimating the direction ofarrival of an emitter. The rotating directional antennas measure thereceived power as a function of the direction, calculating a localmaximum assumed direction of the emitter. The geolocation engine is alsooperable to account for any transient signals that escape detectionbased on rotation speed. This is accomplished by using at least onebroad antenna, reducing the chance of the system missing a signal, aswell as reducing angular resolution. Practical considerations for thesecalculations include, but are not limited to, antenna rotation speed(m), a rate of arrival of signals (γ), and/or a spatial sampling rate(F_(p)s).

The system is further operable to use amplitude ratio methods forgeolocation. These methods involve a multi-lobe amplitude comparison.This is performed using a set of fixed directional antennas pointing indifferent directions. A ratio corresponding to two responses iscalculated, account for antenna patterns. This ratio is used to obtain adirection estimate. By not using moving parts and/or antennas, thesystem is more responsive to transient signals. However, this doesrequire accurate antenna patterns, as these patterns also control systemresolution.

General antenna array processing assumes that a signal, s(t), remainscoherent as it impinges at each antenna in the array. This enables thedelay (τ_(m)) of the signal at an m-th sensor relative to the signal atthe origin of the coordinate system can be expressed as:τ_(m)=−(q _(m) sin(θ)+r _(m) cos(θ))/cWhere c is the propagation of light and Θ is the angle of the signalimpinging in the sensor relative to the r-axis. Since the signal isassumed to have a Taylor series decomposition, the propagation delay,τ_(m), is equivalent to the phase shift of:φ_(m) =−wτ _(M) =>e ^(jφ) ^(m)

Thus, the vector x(t) of antenna responses can be written as:

$\begin{bmatrix}{x_{1}(t)} \\ \vdots \\{x_{M}(t)}\end{bmatrix} = {\begin{bmatrix}e^{j\varphi_{1}} \\ \vdots \\e^{j\varphi_{M}}\end{bmatrix}e^{j({{wt} + \phi})}}$Whereφ_(m)(w,θ)=[q _(m) sin(θ)+r _(m) cos(θ)]w/cMore generally, the sensor has different directionality and frequencycharacteristics which are modeled by applying different gains and phasesto the model above, where the gain and phase of the m-th sensor isdenoted as: g_(m)(w,θ) and φ_(m)(w,θ)

Then, the above equation for x(t) can be expressed as:

$\begin{bmatrix}{x_{1}(t)} \\ \vdots \\{x_{M}(t)}\end{bmatrix} = {{\begin{bmatrix}{{g_{1}\left( {w,\theta} \right)}e^{j{\phi_{1}({w,\theta})}}e^{j\varphi_{1}}} \\ \vdots \\{{g_{M}\left( {w,\theta} \right)}e^{j{\phi_{m}({w,\theta})}}e^{j\varphi_{M}}}\end{bmatrix}e^{j({{wt} + \phi})}} = {{a\left( {w,\theta} \right)}e^{j({{wt} + \phi})}}}$Where α(w,θ) is known as the array response vector.

The collection of all array response vectors for all angles Θ and allfrequencies, w, is known as an array manifold (i.e., a vector space). Ingeneral, if the array manifold is known and it is free of ambiguities,then obtaining the k−1 angles (θ₁ . . . θ_(k-1)) of k−1 signals if theircorresponding array response vector are linearly independent isperformed by correlating x(t) with the array response vector of theappropriate angle. In one embodiment, ambiguities refer to the arraymanifold lacking rank deficiencies to k if the system is trying toresolve k−1 directions at the same frequency. The array manifold doesnot typically have a simple analytical form and thus the array manifoldis approximated using discrete angles for each frequency of interest.

In more general cases, where multiple sinusoidal signals arrive at thearray with additive noise, then the x(t) can be expressed as:

${x(t)} = {{{\underset{i = 1}{\overset{I}{\sum}}{a\left( {w,\theta_{i}} \right)}{s_{i}(t)}} + {n(t){s_{i}(t)}}} = {e^{j({{w_{i}t} + \beta_{i}})} = {{{\left\lbrack {a\left( {w,\theta_{1}} \right)\ \ldots a\left( {w,\theta_{I}} \right)} \right\rbrack\left\lbrack {{s_{1}(t)}\ldots{s_{I}(t)}} \right\rbrack}^{T} + {n(t)}} = {{{A\left( {w,\Theta} \right)}s(t)} + {n(t)}}}}}$

In one embodiment, additive noise refers to thermal noise from sensorsand associated electronics, background noise from the environment,and/or other man-made interference sources including, but not limitedto, diffuse signals.

Where one or more signals are non-sinusoidal (i.e., broadband), theequivalent can be expressed by its Taylor series over the relevantfrequencies. However, when looking for a narrow frequency band ofinterest, the system is operable to assume an array response vector,a(w,θ), is approximately constant with respect to w over all angles, Θ.This implies that the reciprocal of the time required for the signal topropagate across the array is much less than the bandwidth of thesignal. If sensor characteristics do not vary significantly acrossbandwidth, then the dependency on w can be dropped off of the arrayresponse vector and/or matrix, resulting in:x(t)=A(Θ)s(t)+n(t)

For example, in an antenna array using a uniform linear array (ULA), asignal source, s(t)=e^(j(wt+ϕ)), impinges in the ULA at angle Θ. Thus,if the received signal at a first sensor is x₁(t)=s(t), then it isdelayed at sensor m by:

${x_{m}(t)} = {e^{{- j}{w(\frac{{({m - 1})}d\sin{(\theta)}}{c})}}{s(t)}}$

In vector form, this is represented as:

${x(t)} = {{\begin{bmatrix}1 \\e^{{- j}{w(\frac{d\sin{(\theta)}}{c})}} \\ \vdots \\e^{{- j}{w(\frac{{({M - 1})}d\sin{(\theta)}}{c})}}\end{bmatrix}{s(t)}} = {{a\left( {w,\theta} \right)}{s(t)}}}$

If there are source signals received by the ULA, then:

x(t) = A(Θ)s(t) + n(t) Where ${A(\Theta)} = \begin{bmatrix}1 & \ldots & 1 \\e^{{- j}{w(\frac{d\sin{(\theta_{1})}}{c})}} & \ldots & e^{{- j}{w(\frac{d\sin{(\theta_{1})}}{c})}} \\ \vdots & \vdots & \vdots \\e^{{- j}{w(\frac{{({M - 1})}d\sin{(\theta_{1})}}{c})}} & \ldots & e^{{- j}{w(\frac{{({M - 1})}d\sin{(\theta_{1})}}{c})}}\end{bmatrix}$x(t) is the received signal vector (M by 1), s(t)=[s₁(t) . . .s₁(t)]^(T) is the source signal vector (I by 1), n(t) is the noisesignal vector (M by 1), and A(Θ)=[a(w,θ₁), . . . , a(w,θ_(I))] a (M byI) matrix=>Array manifold. In this example, typical assumptions include,but are not limited to, sources of signal(s) are independent and narrowband in relation to dimensions of the ULA (d, Md) and around the samemax frequency, all antenna elements are the same,

$d < \frac{\lambda_{\max}}{2}$to avoid rank ambiguities, the system can resolve M−1 direction angleswithout rank, and/or noises are uncorrelated.

In another example, array processing is performed for DF usingbeamforming. Given knowledge of the array manifold, the array can bemaneuvered by taking linear combinations of each element response. Thisis similar to how a fixed, single antenna can be maneuveredmechanically. Thus, y(t)=w^(H)x(t), where w is interpreted as a FiniteImpulse Response (FIR) of a filter in the spatial domain. To calculatethe power of y(t), assuming a discretization to N samples, the systemuses the following:P _(y) =|

y(n)|²

_(N) =w ^(H)

x(n)x(n)^(H)

_(N) w=W ^(H) R _(xx) wWhere

.

_(N) denotes the time averaging over N sample times and R_(xx) is themeasured spatial autocorrelation matrix of the received array outputdata.

In another example, array processing is performed for DF usingbeamforming, whereR _(xx) =

x(n)x ^(H)(n)

_(n) and R _(xx)=

(A(Θ)s(n)+n(n))(A(Θ)s(n)+n(n))^(H)

_(N)In one embodiment, the system assumes a source signal is uncorrelated toa noise source, resulting in:R _(xx) =A(Θ)R _(ss) A ^(H)(Θ)+R _(nn)Thus, the power of the linear combination and/or spatial filtering ofthe array vector response elements are expressed as:P _(y) =w ^(H)(A(Θ)R _(ss) A ^(H)(Θ)+R _(nn))w

In examples where array processing for DF is performed usingbeamforming, for a single unit magnitude sinusoid impinging the array atangle θ_(o) with no noise becomes:P _(y)(θ)=w ^(H) a(θ_(o))a ^(H)(θ_(o))w=|w ^(H) a(θ_(o))|²

Accounting for the Cauchy-Schwarz inequality|w^(H)a(θ_(o))|²≤∥w∥²∥a(θ_(o))∥², for all vectors w with equality if,and only if, w is proportional to a(θ_(o)), the spatial filter thatmatches the array response at the direction of arrival, θ_(o), producesa maximum value for P_(y)(θ).

In addition, DF can be accomplished by searching over all possibleangles to maximize P_(y)(θ), and/or search over all filters w that areproportional to some array vectors responding to an impinging angle θ,a(θ), where Max {P_(y)(θ)}_(overall angles=>filters w=a(θ)). When thismethod is used, the system behaves like a spinning DF system where theresulting beam is changing for each search angle. Advantageously, thismethod encounters no blind spots due to the rotational and/or rate ofarrival of the source signal.

Moreover, when the system is using beamforming techniques and/orprocesses, the system is operable to search for multiple directions ofarrival of different sources with resolutions depending on the width ofthe beam formed and the height of the sidelobes. For example, a localmaximum of the average filter output power is operable to be shiftedaway from the true direction of arrival (DOA) of a weak signal by astrong source of interference in the vicinity of one of the sidelobes.Alternatively, two closely spaced signals results in only one peak ortwo peaks in the wrong location.

In yet another example, array processing for DF is performed using aCapon Minimum Variance Distortionless Response (MVDR) approach. This isnecessary in cases where multiple source signals are present. The systemobtains more accurate estimates of the DOA when formatting the arraybeam using degrees of freedom to form a beam in the “look” direction andany remaining degrees of freedom to from “nulls” in remainingdirections. The result is a simultaneous beam and null forming filter.Forming nulls in other directions is accomplished by minimizing P_(y)(θ)while constraining a beam in the look direction. This avoids the trivialsolution of w=0. Thus:min_(overall w) P _(y)(θ) subject to w ^(H) a(θ)=1The resulting filter, w_(c)(θ), is shown as:w _(c)(θ)=(a ^(H)(θ)R _(xx) ⁻¹ a(θ))⁻¹ R _(xx) ⁻¹ a(θ)Using this filter, the filter output power is expressed as:P _(yc)(θ)=w _(c) ^(H)(θ)R _(xx) w _(c)(θ)=(a ^(H)(θ)R _(xx) ⁻¹ a(θ))⁻¹

Therefore, the Capon approach searches over all DOA angles that theabove power has maximized, using max_(overall angles)(a^(H)(θ)R_(xx)⁻¹a(θ))⁻¹. A Capon approach is able to discern multiple signal sourcesbecause while looking at signals impinging at 0, the system attenuates asignal arrive at fifteen degrees by a formed beam.

A Capon approach is one method for estimating an angular decompositionof the average power received by the array, sometimes referred to as aspatial spectrum of the array. The Capon approach is a similar approachto spectrum estimation and/or modeling of a linear system.

The system is further operable to employ additional resolutiontechniques including, but not limited to, Multiple Signal Classifier(MUSIC), Estimation of Signal Parameters via Rotational InvarianceTechnique (ESPRITE), and/or any other high-resolution DOA algorithm.These resolution techniques enable the system to find DOAs for multiplesources simultaneously. In addition, these resolution techniquesgenerate high spatial resolution when compared with more traditionalmethods. In one embodiment, these techniques apply only when determiningDOAs for narrowband signal sources.

For example, when using MUSIC-based methods, the system computes an N×Ncorrelation matrix using R_(x)=E{x(t)x^(H)(t)}=AR_(s)A^(H)+σ₀ ²I, whereR_(s)=E{s(t)s^(H)(t)}=diag·{σ₁ ², . . . , σ₁ ²}. If the signal sourcesare correlated so that R_(s) is not diagonal, geolocation will stillwork while R_(s) has full rank. However, if the signal sources arecorrelated such that R_(s) is rank deficient, the system will thendeploy spatial smoothing. This is important, as R_(s) defines thedimension of the signal subspace. However, For N>I, the matrixAR_(s)A^(H) is singular, where det[AR_(s)A^(H)]=det[R_(x)−σ₀ ²I]=0. Butthis implies that σ₀ ² is an eigenvalue of R_(x). Since the dimension ofthe null space AR_(s)A^(H) is N−I, there are N−I such eigenvalues σ₀ ²of R_(x). In addition, since both R_(x) and AR_(s)A^(H) arenon-negative, there are I other eigenvalues σ_(i) ² such that σ_(i) ²>σ₀²>0.

In a preferred embodiment, geolocation is performed using Angle ofArrival (AOA), Time Difference of Arrival (TDOA), Frequency Differenceof Arrival (FDOA), and power distribution ratio measurements.Advantageously, using all four measurements to determine geolocationresults in a more accurate determination of location. In many instances,only one type of geolocation measurement is available that forces theuse of one particular approach (e.g., AOA, TDOA, FDOA), but in manycases geolocation measurements are operable to be derived from behaviorof the signals, thus allowing for the use of multiple measurements(e.g., all four measurements) that are combined to obtain a more robustgeolocation solution. This is especially important when most of themeasurements associated with each approach are extremely noisy.

Learning Engine

In addition, the system includes a learning engine, operable toincorporate a plurality of learning techniques including, but notlimited to, machine learning (ML), artificial intelligence (AI), deeplearning (DL), neural networks (NNs), artificial neural networks (ANNs),support vector machines (SVMs), Markov decision process (MDP), and/ornatural language processing (NLP). The system is operable to use any ofthe aforementioned learning techniques alone or in combination.

Advantageously, the system is operable for autonomous operation usingthe learning engine. In addition, the system is operable to continuouslyrefine itself, resulting in increased accuracy relating to datacollection, analysis, modeling, prediction, measurements, and/or output.

The learning engine is further operable to analyze and/or compute aconditional probability set. The conditional probability set reflectsthe optimal outcome for a specific scenario, and the specific scenariois represented by a data model used by the learning engine. This enablesthe system, when given a set of data inputs, to predict an outcome usinga data model, where the predicted outcome represents the outcome withthe least probability of error and/or a false alarm.

Without a learning engine, prior art systems are still operable tocreate parametric models for predicting various outcomes. However, theseprior art systems are unable to capture all inputs and/or outputs,thereby creating inaccurate data models relating to a specific set ofinput data. This results in a system that continuously produces the sameresults when given completely different data sets. In contrast, thepresent invention utilizes a learning engine with a variety of fastand/or efficient computational methods that simultaneously calculateconditional probabilities that are most directly related to the outcomespredicted by the system. These computational methods are performed inreal-time or near-real-time.

Additionally, the system employs control theory concepts and methodswithin the learning engine. This enables the system to determine ifevery data set processed and/or analyzed by the system represents asufficient statistical data set.

Moreover, the learning engine includes a learning engine softwaredevelopment kit (SDK), enabling the system to prepare and/or manage thelifecycle of datasets used in any system learning application.Advantageously, the learning engine SDK is operable to manage systemresources relating to monitoring, logging, and/or organizing anylearning aspects of the system. This enables the system to train and/orrun models locally and/or remotely using automated ML, AI, DL, and/orNN. The models are operable for configuration, where the system isoperable to modify model configuration parameters and/or training datasets. By operating autonomously, the system is operable to iteratethrough algorithms and/or hyperparameter settings, creating the mostaccurate and/or efficient model for running predictive systemapplications. Furthermore, the learning engine SDK is operable to deploywebservices in order to convert any training models into services thatcan run in any application and/or environment.

Thus, the system is operable to function autonomously and/orcontinuously, refining every predictive aspect of the system as thesystem acquires more data. While this functionality is controlled by thelearning engine, the system is not limited to employing these learningtechniques and/or methods in only the learning engine component, butrather throughout the entire system. This includes RF fingerprinting, RFspectrum awareness, autonomous RF system configuration modification,and/or autonomous system operations and maintenance.

The learning engine uses a combination of physical models andconvolutional neural networks algorithms to compute a set of possibleconditional probabilities depicting the set of all possible outputsbased on input measurements that provide the most accurate prediction ofsolution, wherein accurate means minimizing the false probability of thesolution and also probability of error for the prediction of thesolution.

FIG. 26 is a diagram describing three pillars of a customer missionsolution. The three pillars include environmental awareness, policymanagement, and spectrum management. The system obtains environmentalawareness through a plurality of sensors. The plurality of sensorspreferably captures real-time information about the electromagneticenvironment. Additionally, the system includes machine learning and/orpredictive algorithms to enhance environmental understanding and supportresource scheduling. Policy management is flexible, adaptable, anddynamic, and preferably takes into account real-time information ondevice configurations and the electromagnetic environment. The system ispreferably operable to manage heterogeneous networks of devices andapplications. Spectrum management preferably makes use of advanceddevice capabilities including, but not limited to, directionality,waveforms, hopping, and/or aggregation.

FIG. 27 is a block diagram of one example of a spectrum management tool.The spectrum management tool includes environment information obtainedfrom at least one monitoring sensor and at least one sensor processor.The spectrum management tool further includes a policy manager, areasoner, an optimizer, objectives, device information, and/or a devicemanager. The objectives include information from a mission informationdatabase. The policy manager obtains information from a policyinformation database. In another embodiment, the policy manager usesinformation (e.g., from the policy information database, measurements ofthe electromagnetic environment) to create policies and/or rules forconditional allowance of resources per signal using the spectrum. Thesepolicies and/or rules are then passed to the reasoner to determineoptimization conditional constraints to be used by the optimizer withthe goal of optimizing the utilization of the spectrum (e.g., based onmission information and objectives) by all signals present according tothe policies and/or rules. At the output of the optimizer, resources(bandwidth, power, frequency, modulation, spatial azimuth and elevationfocus for transmitter/receiver (TX/RX) sources) as well as interferencelevels per application is recommended for each signal source. Afterthat, the loop of collecting and environmental awareness is fed to thepolicy manger and the reasoner.

FIG. 28 is a block diagram of one embodiment of a resource brokerageapplication. As previously described, the resource brokerage applicationis preferably operable to use processed data from the at least onemonitoring sensor and/or additional information to determineenvironmental awareness (e.g., environmental situational awareness). Theenvironmental awareness and/or capabilities of a device and/or aresource are used to determine policies and/or reasoning to optimize thedevice and/or the resource. The resource brokerage application isoperable to control the device and/or the resource. Additionally, theresource brokerage application is operable to control the at least onemonitoring sensor.

Semantic Engine

The system further includes an automated semantic engine and/ortranslator as shown in FIG. 29 . The translator is operable to receivedata input including, but not limited to, at least one use case, atleast one objective, and/or at least one signal. In one embodiment, theat least one use case is a single signal use case. In anotherembodiment, the at least one use case is a multiple-signal use case.Once the translator receives data input, the translator uses naturallanguage processing (NLP), and/or similar data translation processes andtechniques, to convert the data input into actionable data for theautomated semantic engine.

By separating the data translation process from the automated semanticengine, the system is operable to provide more processing power once thedata input is sent to the automated semantic engine, reducing theoverall processing strain on the system.

The automated semantic engine includes a rule component, a syntaxcomponent, a logic component, a quadrature (Q) component, and/or aconditional set component. In addition, the semantic engine is operablefor network communication with a prior knowledge database, an analyticsengine, and/or a monitoring and capture engine. Data is initially sentto the automated semantic engine via the translator. The automatedsemantic engine is operable to receive data from the translator in formsincluding, but not limited to, audio data, text data, video data, and/orimage data. In one embodiment, the automated semantic engine is operableto receive a query from the translator. The logic component and/or therule component are operable to establish a set of system rules and/or aset of system policies, where the set of system rules and/or the set ofsystem policies is created using the prior knowledge database.

Advantageously, the automated semantic engine is operable to runautonomously using any of the aforementioned learning and/or automationtechniques. This enables the system to run continuously, withoutrequiring user interaction and/or input, resulting in a system that isconstantly learning and/or refining data inputs, creating more accuratepredictions, models, and/or suggested actions.

Moreover, the automated semantic engine enables the system to receivequeries, searches, and/or any other type of search-related functionusing natural language, as opposed to requiring a user and/or customerto adapt to a particular computer language. This functionality isperformed using a semantic search via natural language processing (NLP).The semantic search combines traditional word searches with logicalrelationships and concepts.

In one embodiment, the automated semantic engine uses Latent SemanticIndexing (LSI) within the automated semantic engine. LSI organizesexisting information within the system into structures that supporthigh-order associations of words with text objects. These structuresreflect the associative patterns found within data, permitting dataretrieval based on latent semantic context in existing system data.Furthermore, LSI is operable to account for noise associated with anyset of input data. This is done through LSI's ability to increase recallfunctionality, a constraint of traditional Boolean queries and vectorspace models. LSI uses automated categorization, assigning a set ofinput data to one or more predefined data categories contained withinthe prior knowledge database, where the categories are based on aconceptual similarity between the set of input data and the content ofthe prior knowledge database. Furthermore, LSI makes use of dynamicclustering, grouping the set of input data to data within the priorknowledge database using conceptual similarity without using exampledata to establish a conceptual basis for each cluster.

In another embodiment, the automated semantic engine uses LatentSemantic Analysis (LSA) within the automated semantic engine. LSAfunctionalities include, but are not limited to, occurrence matrixcreation, ranking, and/or derivation. Occurrence matrix creationinvolves using a term-document matrix describing the occurrences ofterms in a set of data. Once the occurrence matrix is created, LSA usesranking to determine the most accurate solution given the set of data.In one embodiment, low-rank approximation is used to rank data withinthe occurrence matrix.

In another embodiment, the automated semantic engine uses semanticfingerprinting. Semantic fingerprinting converts a set of input datainto a Boolean vector and creates a semantic map using the Booleanvector. The semantic map is operable for use in any context and providesan indication of every data match for the set of input data. Thisenables the automated semantic engine to convert any set of input datainto a semantic fingerprint, where semantic fingerprints are operable tocombine with additional semantic fingerprints, providing an accuratesolution given the set of input data. Semantic fingerprint functionalityfurther includes, but is not limited to, risk analysis, document search,classifier indication, and/or classification.

In yet another embodiment, the automated semantic engine uses semantichashing. By using semantic hashing, the automated semantic engine maps aset of input data to memory addresses using a neural network, wheresemantically similar sets of data inputs are located at nearbyaddresses. The automated semantic engine is operable to create agraphical representation of the semantic hashing process using countingvectors from each set of data inputs. Thus, sets of data inputs similarto a target query can be found by accessing all of the memory addressesthat differ by only a few bits from the address of the target query.This method extends the efficiency of hash-coding to approximatematching much faster than locality sensitive hashing.

In one embodiment, the automated semantic engine is operable to create asemantic map. The semantic map is used to create target data at thecenter of the semantic map, while analyzing related data and/or datawith similar characteristics to the target data. This adds a secondarylayer of analysis to the automated semantic engine, providing secondarycontext for the target data using similar and/or alternative solutionsbased on the target data. The system is operable to create avisualization of the semantic map.

Traditional semantic network-based search systems suffer from numerousperformance issues due to the scale of an expansive semantic network. Inorder for the semantic functionality to be useful in locating accurateresults, a system is required to store a high volume of data. Inaddition, such a vast network creates difficulties in processing manypossible solutions to a given problem. The system of the presentinvention solves these limitations through the various learningtechniques and/or processes incorporated within the system. Whencombined with the ability to function autonomously, the system isoperable to process a greater amount of data than systems making use ofonly traditional semantic approaches.

By incorporating the automated semantic engine within the system, thesystem has a greater understanding of potential solutions, given aprovided set of data. Semantic engines are regularly associated withsemantic searches or searches with meaning or searches withunderstanding of overall meaning of the query, thus by understanding thesearcher's intent and contextual meaning of the search to generate morerelevant results. Semantic engines of the present invention, along witha spectrum specific ontology (vocabulary and operational domainknowledge), help automate spectrum utilization decisions based ondynamic observations and extracted environmental awareness, and createand extend spectrum management knowledge for multiple applications.

Tip and Cue Processes

The system uses a set of “tip and cue” processes, generally referring todetection, processing, and/or providing alerts using creating actionabledata from acquired RF environmental awareness information in conjunctionwith a specific rule set, further enhancing the optimizationcapabilities of the system. The specific rule set is translated intooptimization objectives, including constraints associated with signalcharacteristics. The tip and cue processes of the present inventionproduce actionable data to solve a plurality of user issues and/orobjectives.

Tip and cue processes are performed by an awareness system. Theawareness system is operable to receive input data including, but notlimited to, a set of use cases, at least one objective, and/or a ruleset. The input data is then analyzed by a translator component, wherethe translator component normalizes the input data. Once normalized, theinput data is sent to a semantic engine. The semantic engine isnecessary for analyzing unstructured data inputs. Thus, a semanticengine is necessary to understand data inputs and apply contextualanalysis as well, resulting in a more accurate output result. Thisaccuracy is primarily accomplished using the previously mentionedlearning techniques and/or technologies.

The semantic engine uses the input data to create a set of updatedrules, a syntax, a logic component, a conditional data set, and/orQuadrature (Q) data. The semantic engine is operable for networkcommunication with components including, but not limited to, a priorknowledge database, an analytics engine, and/or a monitoring and captureengine. The monitoring and capture engine operates with an RFenvironment and includes a customer application programming interface(API), a radio server, and/or a coverage management component. Thecustomer API and the radio server are operable to output a set ofI-phase and Q-phase (I/Q) data using a Fast Fourier Transform (FFT). Theset of I/Q data demonstrates the changes in amplitude and phase in asine wave. The monitor and capture engine also serves as an optimizationpoint for the system.

The awareness engine operates as both a platform optimization unit and aclient optimization unit. The awareness engine is operable to performfunctions including, but not limited to, detection, classification,demodulation, decoding, locating, and/or signaling alarms. The detectionand/or classification functions assist with incoming RF data acclimationand further includes a supervised learning component, where thesupervised learning component is operable to make use of any of theaforementioned learning techniques and/or technologies. The demodulationand/or decode functionalities are operable to access RF data from WIFI,Land Mobile Radio (LMR), Long Term Evolution (LTE) networks, and/orUnmanned Aircraft Systems (UAS). The location component of the awarenessengine is operable to apply location techniques including, but notlimited to, DF, geolocation, and/or Internet Protocol (IP) basedlocation. The awareness engine is operable to signal alarms using FASDand/or masks. In one embodiment, the masks are dynamic masks.

The analytics engine is operable to perform functions including, but notlimited to, data qualification, data morphing, and/or data computing.

The awareness engine, analytics engine, and the semantic engine are alloperable for network communication with the prior knowledge database.This enables each of the previously mentioned engines to compare inputand/or output with data already processed and analyzed by the system.

The various engines present within the Tip & Cue process furtheroptimize client output in the form of dynamic spectrum utilizationand/or allocation. The system uses the Tip & Cue process to provideactionable information and/or actionable knowledge to be utilized by atleast one application to mitigate problems of the at least oneapplication and/or to optimize services or goals of the at least oneapplication.

In a preferred embodiment, each customer has a service level agreement(SLA) with the system manager that specifies usage of the spectrum. Thesystem manager is operable act as an intermediary between a firstcustomer and a second customer in conflicts regarding the spectrum. Ifsignals of the first customer interfere with signals of the secondcustomer in violation of one or more of SLAs, the system is operable toprovide an alert to the violation. Data regarding the violation isstored in at least one database within the system, which facilitatesresolution of the violation. The control plane is operable to directlycommunicate the first customer (i.e., customer in violation of SLA)and/or at least one base station to modify parameters to resolve theviolation.

In one embodiment, the system is used to protect at least one criticalasset. Each of the at least one critical asset is within a protectionarea. For example, a first critical asset is within a first protectionarea, a second critical asset is within a second protection area, etc.In one embodiment, the protection area is defined by sensor coveragefrom the at least one monitoring sensor. In other embodiments, theprotection area is defined by sensor coverage from the at least onemonitoring sensor, a geofence, and/or GPS coordinates. The system isoperable to detect at least one signal within the protection area andsend an alarm for the at least one signal when outside of allowedspectrum use within the protection area.

The system is further operable to determine what information isnecessary to provide actionable information. For example, sensorprocessing requires a large amount of power. Embedding only the sensorsrequired to provide sufficient variables for customer goals reducescomputational and/or power requirements.

FIGS. 30-32 are flow diagrams illustrating the process of obtainingactionable data and using knowledge decision gates. FIG. 30 illustratesa flow diagram of a method to obtain actionable data based on customergoals 3000. A goal is rephrased as a question in Step 3002. Informationrequired to answer the question is identified in Step 3004. Next,quality, quantity, temporal, and/or spatial attributes are identifiedfor each piece of information in Step 3006. In a preferred embodiment,all four attributes (i.e., quality, quantity, temporal, and spatial) areidentified in Step 3006. The quality, quantity, temporal, and/or spatialattributes are ranked by importance in Step 3008. For each informationand attribute pair, corresponding physical layer information from thewireless environment is associated in Step 3010. All informationobtained in steps 3004-3010 is operable to be transmitted to thesemantic engine.

Further, wireless information is associated with a most statisticallyrelevant combination of extracted measurements in at least one dimensionin Step 3012. The at least one dimension includes, but is not limitedto, time, frequency, signal space and/or signal characteristics,spatial, and/or application goals and/or customer impact. In a preferredembodiment, the at least one dimension includes time, frequency, signalspace and/or signal characteristics, spatial, and application goalsand/or customer impact. The RF awareness measurements are then qualifiedin Step 3014 and actionable data is provided in Step 3016 based on therelationship established in Steps 3002-3012. Actionable data efficiencyis qualified in Step 3018 based on Step 3014. All actionable data andits statistical significance is provided in Step 3020.

FIG. 31 illustrates a flow diagram of a method of implementation ofactionable data and knowledge decision gates from total signal flow3100. A customer goal is rephrased as a question in Step 3102. Thecustomer goal is provided to the semantic engine having a properdictionary in Step 3104 (as shown in Steps 3002-3012 of FIG. 30 ).Constraints with statistical relevance from Step 3104 and extractedelectromagnetic (e.g., RF) awareness information from sensors in Step3106 are used in an optimization cost function in Step 3108 (as shown inStep 3014 of FIG. 30 ). Results from the optimization cost function inStep 3108 are provided to an optimization engine in Step 3110 (as shownin Steps 3016-3020 of FIG. 30 ) to provide actionable data and itsstatistical relevance in Step 3112.

FIG. 32 illustrates a flow diagram of a method to identify knowledgedecision gates based on operational knowledge 3200. Customer operationaldescription of utilization of actionable data is provided in Step 3202.The customer operational description of utilization of actionable datafrom Step 3202 is used identify a common state of other information usedto express the customer operational description and/or required to makedecisions in Step 3204. Further, the customer operational description ofutilization of actionable data from Step 3202 is used to provideparameterization of customer operational utilization of actionable datain Step 3206. The parameterization of customer operational utilizationof actionable data from Step 3206 is used to identify conditions andcreate a conditional tree in Step 3208. In one embodiment, theinformation from Step 3204 is used to identify the conditions and createthe conditional tree in Step 3208. Information from Steps 3206-3208 isoperable to be transmitted to the semantic engine. Actionable data isprovided in Step 3210 and used to compute statistical properties of theactionable data as it changes over time in Step 3212. Information fromSteps 3208 and 3212 is used by a decision engine to travel a decisiontree to identify decision gates in Step 3214. The identified decisiongates from Step 3214 are provided along with the information in Step3204 to allow the customer to make decisions in Step 3216.

FIG. 33 illustrates an overview of one example of information used toprovide knowledge. Information including, but not limited to, networkinformation (e.g., existing site locations, existing siteconfigurations), real estate information (e.g., candidate sitelocations), signal data (e.g., LTE demodulation), signal sites, siteissues, crowdsourced information (e.g., geographic trafficdistribution), and/or geographic information services (GIS) is used toperform propagation modeling. The propagation models are used toevaluate candidate results and expected impact from any changes (e.g.,addition of macrosites, tower). In one embodiment, additional analysisis performed on the candidate results and/or the expected impact.

Example One

In one example, the system is used by a tower company to evaluate if acarrier's performance can be improved by placing at least one additionalmacrosite on at least one additional tower. If the evaluation shows thatthe carrier's performance can be improved, it supports a pitch from thetower company to place the at least one macrosite on the at least oneadditional tower, which would generate revenue for the tower company.

FIG. 34 is a map showing locations of three macrosites (“1” (green), “2”(orange), and “3” (purple)), 3 SigBASE units (orange diamond), and aplurality of locations evaluated for alternate or additional sitedeployment (green circles).

FIG. 35 is a graph of distribution of users by average downlink PhysicalResource Block (PRB) allocation. Real-time monitoring shows downlinkresources allocated to each user. Allocations occur many times persecond. A significant concentration of users on 739 MHz are allocatedresources for voice service. Most users on 2165 MHz are allocatedresources common for high-speed data.

FIG. 36 illustrates rate of overutilization events and degree ofoverutilization. Real-time monitoring shows the percentage of downlinkresources utilized when utilization exceeded 50%. Utilization statisticsare generated per second as configured. The rate at which a sectorutilization exceeds 50% (overutilized) is presented by hour. the averageutilization levels when overutilization occurs describes the severity.

FIG. 37A is a sector coverage map for the three macrosites (“1” (green),“2” (orange), and “3” (purple)).

FIG. 37B illustrates signal strength for the sector shown in FIG. 37A.This figure displays areas of poor coverage.

FIG. 37C illustrates subscriber density for the sector shown in FIG.37A. In one embodiment, data from external sources is used to determinesubscriber distribution and density. This figure displays areas of highsubscriber demand.

FIG. 37D illustrates carrier-to-interference ratio for the sector shownin FIG. 37A. This figure displays areas of poor quality.

FIG. 38A illustrates the baseline scenario shown in FIG. 34 . FIG. 38Bis a map showing locations of the three original macrosites (“1”(green), “2” (orange), and “3” (purple)) and two additional macrosites(“4” (dark blue) and “5” (light blue)).

FIG. 39 illustrates signal strength of the baseline scenario from FIG.38A on the left and the scenario with two additional macrosites fromFIG. 38B on the right. The addition of a 2-sector eNodeB to a towerincreases expected coverage by 3 km² as shown in Table 2 below. A totalservice area for the baseline is 9.89 km² and the total service areaincreases to 13.15 km² with the two additional macrosites. A total areawith a carrier-to-interference ratio less than 5 dB decreases from 1.10km² for the baseline to 0.38 km² with the two additional macrosites. Atotal area with a carrier-to-interference ratio greater than 5 dBincreases from 8.79 km² for the baseline to 12.77 km² with the twoadditional macrosites. Traffic served without harmful interferenceincreases from 16.73 Erlands for the baseline to 25.23 Erlands with thetwo additional macrosites. Additionally, an increase in traffic servedof 40% is expected. Further utilization reduction of 30% is expected forpre-existing sectors. Areas of poor coverage are also reduced.

TABLE 2 2-sector Metric Baseline site added Total service area, sq km9.89 13.15 Total area with C/I <5 dB, km² 1.10 0.38 Total area withC/I >5 dB, km² 8.79 12.77 Traffic served without harmful interference,16.73 25.23 Erlands

FIG. 40A illustrates carrier-to-interference ratio of the baselinescenario from FIG. 38A. FIG. 40B illustrates carrier-to-interferenceratio of the scenario with two additional macrosites. The additional twomacrosites reduce areas with poor carrier-to-interference.

Example Two

In a second example, the system is also used by a tower company toevaluate if a carrier's performance can be improved by placing at leastone additional macrosite on at least one additional tower. If theevaluation shows that the carrier's performance can be improved, itsupports a pitch from the tower company to place the at least onemacrosite on the at least one additional tower, which would generaterevenue for the tower company.

FIG. 41 illustrates a baseline scenario for the second example on theleft and a map showing locations of the original macrosites from thebaseline scenario with three additional proposed macrosites on theright.

FIG. 42 illustrates signal strength of the baseline scenario from FIG.41 on the left and the scenario with three additional proposedmacrosites from FIG. 41 on the right. The addition of a 3-sector eNodeBto a tower increases expected coverage by 0.5 km² as shown in Table 3below. A total service area for the baseline is 21.3 km² and the totalservice area increases to 21.8 km² with the three additional macrosites.A total area with a carrier-to-interference ratio less than 5 dBincreases from 3.0 km² for the baseline to 3.1 km² with the threeadditional macrosites. A total area with a carrier-to-interference ratiogreater than 5 dB increases from 18.3 km² for the baseline to 18.7 km²with the three additional macrosites. Traffic served without harmfulinterference increases from 79.7 Erlands for the baseline to 80.9Erlands with the three additional macrosites. Additionally, an increasein traffic served of 2% is expected. Further utilization reduction of 2%is expected for pre-existing sectors.

TABLE 3 487044 Metric Baseline Added Total service area, sq km 21.3 21.8Total area with C/I <5 dB, km² 3.0 3.1 Total area with C/I >5 dB, km²18.3 18.7 Traffic served without harmful interference, Erlands 79.7 80.9

FIG. 43 illustrates carrier-to-interference ratio of the baselinescenario from FIG. 41 on the left and carrier-to-interference ratio ofthe scenario with three additional proposed macrosites from FIG. 41 onthe right. The three additional proposed macrosites slightly reduceareas with poor carrier-to-interference.

Although adding the 3-sector eNodeB does slightly improve performance,this performance improvement is not significant enough to support theaddition of the three proposed macrosites to the tower.

Example Three

In a third example, the system is used to evaluate which carrierprovides better service.

FIG. 44 illustrates a signal strength comparison of a first carrier(“Carrier 1”) with a second carrier (“Carrier 2”) for 700 MHz.

FIG. 45 illustrates carrier-to-interference ratio for Carrier 1 andCarrier 2.

FIG. 46 is a graph of Area vs. RSSI and Traffic vs. RSSI for Carrier 1and Carrier 2. Carrier 1 and Carrier 2 serve approximately the sameamount of area in the sector.

FIG. 47 is a graph of traffic difference for Carrier 1 versus Carrier 2.Carrier 2 serves more traffic than Carrier 1 at the extremes ofcoverage, while Carrier 1 serves more traffic in the middle range ofcoverage.

FIGS. 44-47 illustrate traffic composition for each SigBASE. Differenttraffic types require different signal-to-noise ratios (SNRs) vsreference signals received power (RSRP). For voice traffic, the SNR isfrom −6 dB to 0 dB, while the SNR goes upwards of 20 dB for streamingvideo.

FIG. 48 is a graph of SNR vs. RSRP for each SigBASE for the thirdexample.

FIG. 49 is another graph of SNR vs. RSRP for each SigBASE for the thirdexample.

FIG. 50 is a clustered graph of SNR vs. RSRP for each SigBASE for thethird example.

FIG. 51 is another clustered graph of SNR vs. RSRP for each SigBASE forthe third example.

FIG. 52 is a schematic diagram of an embodiment of the inventionillustrating a computer system, generally described as 800, having anetwork 810, a plurality of computing devices 820, 830, 840, a server850, and a database 870.

The server 850 is constructed, configured, and coupled to enablecommunication over a network 810 with a plurality of computing devices820, 830, 840. The server 850 includes a processing unit 851 with anoperating system 852. The operating system 852 enables the server 850 tocommunicate through network 810 with the remote, distributed userdevices. Database 870 is operable to house an operating system 872,memory 874, and programs 876.

In one embodiment of the invention, the system 800 includes a network810 for distributed communication via a wireless communication antenna812 and processing by at least one mobile communication computing device830. Alternatively, wireless and wired communication and connectivitybetween devices and components described herein include wireless networkcommunication such as WI-FI, WORLDWIDE INTEROPERABILITY FOR MICROWAVEACCESS (WIMAX), Radio Frequency (RF) communication including RFidentification (RFID), NEAR FIELD COMMUNICATION (NFC), BLUETOOTHincluding BLUETOOTH LOW ENERGY (BLE), ZIGBEE, Infrared (IR)communication, cellular communication, satellite communication,Universal Serial Bus (USB), Ethernet communications, communication viafiber-optic cables, coaxial cables, twisted pair cables, and/or anyother type of wireless or wired communication. In another embodiment ofthe invention, the system 800 is a virtualized computing system capableof executing any or all aspects of software and/or applicationcomponents presented herein on the computing devices 820, 830, 840. Incertain aspects, the computer system 800 is operable to be implementedusing hardware or a combination of software and hardware, either in adedicated computing device, or integrated into another entity, ordistributed across multiple entities or computing devices.

By way of example, and not limitation, the computing devices 820, 830,840 are intended to represent various forms of electronic devicesincluding at least a processor and a memory, such as a server, bladeserver, mainframe, mobile phone, personal digital assistant (PDA),smartphone, desktop computer, netbook computer, tablet computer,workstation, laptop, and other similar computing devices. The componentsshown here, their connections and relationships, and their functions,are meant to be exemplary only, and are not meant to limitimplementations of the invention described and/or claimed in the presentapplication.

In one embodiment, the computing device 820 includes components such asa processor 860, a system memory 862 having a random access memory (RAM)864 and a read-only memory (ROM) 866, and a system bus 868 that couplesthe memory 862 to the processor 860. In another embodiment, thecomputing device 830 is operable to additionally include components suchas a storage device 890 for storing the operating system 892 and one ormore application programs 894, a network interface unit 896, and/or aninput/output controller 898. Each of the components is operable to becoupled to each other through at least one bus 868. The input/outputcontroller 898 is operable to receive and process input from, or provideoutput to, a number of other devices 899, including, but not limited to,alphanumeric input devices, mice, electronic styluses, display units,touch screens, signal generation devices (e.g., speakers), or printers.

By way of example, and not limitation, the processor 860 is operable tobe a general-purpose microprocessor (e.g., a central processing unit(CPU)), a graphics processing unit (GPU), a microcontroller, a DigitalSignal Processor (DSP), an Application Specific Integrated Circuit(ASIC), a Field Programmable Gate Array (FPGA), a Programmable LogicDevice (PLD), a controller, a state machine, gated or transistor logic,discrete hardware components, or any other suitable entity orcombinations thereof that can perform calculations, process instructionsfor execution, and/or other manipulations of information.

In another implementation, shown as 840 in FIG. 52 , multiple processors860 and/or multiple buses 868 are operable to be used, as appropriate,along with multiple memories 862 of multiple types (e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core).

Also, multiple computing devices are operable to be connected, with eachdevice providing portions of the necessary operations (e.g., a serverbank, a group of blade servers, or a multi-processor system).Alternatively, some steps or methods are operable to be performed bycircuitry that is specific to a given function.

According to various embodiments, the computer system 800 is operable tooperate in a networked environment using logical connections to localand/or remote computing devices 820, 830, 840 through a network 810. Acomputing device 830 is operable to connect to a network 810 through anetwork interface unit 896 connected to a bus 868. Computing devices areoperable to communicate communication media through wired networks,direct-wired connections or wirelessly, such as acoustic, RF, orinfrared, through an antenna 897 in communication with the networkantenna 812 and the network interface unit 896, which are operable toinclude digital signal processing circuitry when necessary. The networkinterface unit 896 is operable to provide for communications undervarious modes or protocols.

In one or more exemplary aspects, the instructions are operable to beimplemented in hardware, software, firmware, or any combinationsthereof. A computer readable medium is operable to provide volatile ornon-volatile storage for one or more sets of instructions, such asoperating systems, data structures, program modules, applications, orother data embodying any one or more of the methodologies or functionsdescribed herein. The computer readable medium is operable to includethe memory 862, the processor 860, and/or the storage media 890 and isoperable be a single medium or multiple media (e.g., a centralized ordistributed computer system) that store the one or more sets ofinstructions 900. Non-transitory computer readable media includes allcomputer readable media, with the sole exception being a transitory,propagating signal per se. The instructions 900 are further operable tobe transmitted or received over the network 810 via the networkinterface unit 896 as communication media, which is operable to includea modulated data signal such as a carrier wave or other transportmechanism and includes any delivery media. The term “modulated datasignal” means a signal that has one or more of its characteristicschanged or set in a manner as to encode information in the signal.

Storage devices 890 and memory 862 include, but are not limited to,volatile and non-volatile media such as cache, RAM, ROM, EPROM, EEPROM,FLASH memory, or other solid state memory technology; discs (e.g.,digital versatile discs (DVD), HD-DVD, BLU-RAY, compact disc (CD), orCD-ROM) or other optical storage; magnetic cassettes, magnetic tape,magnetic disk storage, floppy disks, or other magnetic storage devices;or any other medium that can be used to store the computer readableinstructions and which can be accessed by the computer system 800.

In one embodiment, the computer system 800 is within a cloud-basednetwork. In one embodiment, the server 850 is a designated physicalserver for distributed computing devices 820, 830, and 840. In oneembodiment, the server 850 is a cloud-based server platform. In oneembodiment, the cloud-based server platform hosts serverless functionsfor distributed computing devices 820, 830, and 840.

In another embodiment, the computer system 800 is within an edgecomputing network. The server 850 is an edge server, and the database870 is an edge database. The edge server 850 and the edge database 870are part of an edge computing platform. In one embodiment, the edgeserver 850 and the edge database 870 are designated to distributedcomputing devices 820, 830, and 840. In one embodiment, the edge server850 and the edge database 870 are not designated for distributedcomputing devices 820, 830, and 840. The distributed computing devices820, 830, and 840 connect to an edge server in the edge computingnetwork based on proximity, availability, latency, bandwidth, and/orother factors.

It is also contemplated that the computer system 800 is operable to notinclude all of the components shown in FIG. 52 , is operable to includeother components that are not explicitly shown in FIG. 52 , or isoperable to utilize an architecture completely different than that shownin FIG. 52 . The various illustrative logical blocks, modules, elements,circuits, and algorithms described in connection with the embodimentsdisclosed herein are operable to be implemented as electronic hardware,computer software, or combinations of both. To clearly illustrate thisinterchangeability of hardware and software, various illustrativecomponents, blocks, modules, circuits, and steps have been describedabove generally in terms of their functionality. Whether suchfunctionality is implemented as hardware or software depends upon theparticular application and design constraints imposed on the overallsystem. Skilled artisans may implement the described functionality invarying ways for each particular application (e.g., arranged in adifferent order or partitioned in a different way), but suchimplementation decisions should not be interpreted as causing adeparture from the scope of the present invention.

The above-mentioned examples are provided to serve the purpose ofclarifying the aspects of the invention, and it will be apparent to oneskilled in the art that they do not serve to limit the scope of theinvention. By nature, this invention is highly adjustable, customizableand adaptable. The above-mentioned examples are just some of the manyconfigurations that the mentioned components can take on. Allmodifications and improvements have been deleted herein for the sake ofconciseness and readability but are properly within the scope of thepresent invention.

The invention claimed is:
 1. A system for spectrum analysis in anelectromagnetic environment comprising: at least one monitoring sensorincluding at least one receiver channel operable to monitor theelectromagnetic environment and create measured data based on theelectromagnetic environment; a radio receiver front-end subsystemconfigured to process the measured data, thereby creating processeddata; a frequency domain programmable channelizer configured to analyzethe processed data; an in-phase and quadrature (I/Q) buffer; a blinddetection engine; and a noise floor estimator.
 2. The system of claim 1,wherein one or more of the at least one monitoring sensor is mounted ona drone, on a vehicle, in or on a street light, in or on a traffic pole,and/or on top of a building.
 3. The system of claim 1, wherein thefrequency domain programmable channelizer includes a comparison at eachof the at least one receiver channel, and wherein the comparisonprovides anomalous detection using a mask with frequency and power. 4.The system of claim 1, wherein the frequency domain programmablechannelizer includes channelization selector logic for a table lookup offilter coefficient and channelization vectors.
 5. The system of claim 1,wherein the radio receiver front-end subsystem includes aradio-frequency integrated circuit (RFIC), a digital down-converter(DDC), and a field programmable gate array (FPGA).
 6. The system ofclaim 1, further including at least one data analysis engine, a semanticengine, and/or an optimization engine.
 7. The system of claim 1, whereindata from the frequency domain programmable channelizer is operable tocalculate a power spectral density (PSD).
 8. The system of claim 7,wherein the PSD is transmitted to the noise floor estimator.
 9. Thesystem of claim 1, wherein the frequency domain programmable channelizerincludes at least one channel definition, at least one channelizationvector, at least one fast Fourier Transform (FFT) configuration, atleast one deference matrix, at least one detector configuration, and/orat least one channel detection.
 10. The system of claim 1, wherein thenoise floor estimator is operable to estimate a bin-wise noise model,estimate a bin-wise noise plus signal model, determine a bin-levelprobability of false alarm, a bin-level threshold, a channel-levelprobability of false alarm, a channel-level level threshold, calculate adetection vector, count a number of elements above the bin-levelthreshold, determine a probability of false alarm, determine aprobability of missed detection, and/or determine an overall detectionprobability.
 11. The system of claim 1, wherein the blind detectionengine is operable to estimate a number of channels, correspondingbandwidths for the number of channels, and center frequencies using anaveraged power spectral density (PSD) of at least one signal ofinterest.
 12. The system of claim 1, further including a classificationengine, wherein the classification engine is operable to generate aquery to a static database to classify at least one signal of interestbased on information from the frequency domain programmable channelizer.13. A system for spectrum analysis in an electromagnetic environmentcomprising: at least one monitoring sensor including at least onereceiver channel operable to monitor the electromagnetic environment andcreate measured data based on the electromagnetic environment; a radioreceiver front-end subsystem configured to process the measured data,thereby creating processed data; a frequency domain programmablechannelizer configured to analyze the processed data; and an in-phaseand quadrature (UQ) buffer; wherein the frequency domain programmablechannelizer includes at least one channel definition, at least onechannelization vector, at least one fast Fourier Transform (FFT)configuration, at least one deference matrix, at least one detectorconfiguration, and/or at least one channel detection.
 14. The system ofclaim 13, wherein the at least one deference matrix is operable toidentify at least one narrowband channel that is a subset of at leastone wideband channel.
 15. The system of claim 13, wherein the at leastone FFT configuration is operable to resolve ambiguities between atleast two channels by employing a sufficient resolution bandwidth. 16.The system of claim 13, wherein the at least one channelization vectoris operable to specify normalized power levels per FFT bin for at leastone channel.
 17. The system of claim 13, wherein power levels of the atleast one channelization vector are normalized with respect to peakpower in a spectrum envelope of at least one channel.
 18. The system ofclaim 13, wherein the at least one detector configuration includes aminimum acceptable probability of false alarm and/or a minimumacceptable probability of missed detection.
 19. The system of claim 13,wherein the at least one channel detection is operable to perform ahypothesis test for at least one bin using information from a noisefloor estimator and a maximum probability of false alarm.
 20. A systemfor spectrum analysis in an electromagnetic environment comprising: atleast one monitoring sensor including at least one receiver channeloperable to monitor the electromagnetic environment and create measureddata based on the electromagnetic environment; a radio receiverfront-end subsystem configured to process the measured data, therebycreating processed data; a frequency domain programmable channelizerconfigured to analyze the processed data; an in-phase and quadrature(I/Q) buffer; and a classification engine; wherein the classificationengine is operable to generate a query to a static database to classifyat least one signal of interest based on information from the frequencydomain programmable channelizer.